Culture and Methodologies Resources

DZone's Featured Culture and Methodologies Resources

From Engineer to Leader: Scaling Impact Beyond Code

By Kushal Thakkar

Back in college, I thought that technical excellence was everything. But after a decade in the tech industry, I've learned that leadership requires a completely different mindset. One day you're sitting there writing code and shipping features, and suddenly you're making project plans, helping your teammates, and thinking about the bigger picture. Moving from being an IC to a tech lead can feel like a big and uncomfortable jump. Let me share what I've learned about growing beyond just coding through some real examples from my journey. The Mindset Shift: From Doing to Enabling When I first became a tech lead at a big tech company here in the US, my first instinct was to jump in and do all the important and urgent projects myself. I was known as the person who could fix any problem. My manager saw this and called me for a 1:1 about my "new" role. He told me that my actual job now was to help someone else on my team execute, not to do everything myself. This was not easy for me to accept at first. I worked with an engineer on my team, helped her understand the plan, and guided her till she could execute it perfectly. This got her promoted, and I learned something really important: my job was to enable others to become successful. Instead of just owning your piece of code, you need to look at everything — making sure we're solving the right problems and the team has everything they need. Influencing Beyond Your Team Consider a scenario where your team needs to use another team's API, but that API has some problems. As an engineer, you'd probably just raise a ticket and wait. But as a tech lead, you need to actively talk to the other team's lead to influence their priorities. Once, I had to convince another team to fix some major reliability issues. I collected data about all the problems we were facing and showed them how it was affecting our customers. I explained how fixing these issues would help both teams. Because I presented it properly, they added it to their next quarter's plans. To influence people when you don't have direct authority, you need to: Build good relationships with other leads, managers, and PMsUse actual data and customer feedback to support what you're sayingShow how your ideas will help the organizationBe ready to adjust your approach and find common ground Resolving Conflicts in Complex Organizations In one instance, two teams had fundamentally different approaches to integrating a new feature. Our team wanted a quick solution to meet a deadline; the platform team pushed for a more robust approach that would take longer. As tech lead, I organized a joint meeting where each side could explain their concerns. We discussed both approaches and talked about the pros and cons. Eventually, we discovered a third approach — using feature flags to implement a quick solution while collaboratively building the long-term one. We left with not just a plan, but a better understanding of each other's constraints. Tech leads must develop strong conflict resolution skills: Listen actively to all perspectivesFocus on finding common groundGuide discussions toward solutions rather than blameKnow when and how to escalate appropriatelyMaintain professional relationships throughout disagreements Dealing With Ambiguity Tech leads often face poorly defined problems — if they were straightforward, someone would have solved them already! In one instance, I noticed our team's developer productivity declining but couldn't figure out the exact issues. Instead of making assumptions, I: Conducted detailed interviews with engineers about recent projectsRan a team survey to identify specific pain pointsAnalyzed metrics for top three pain points (build pipelines, testing infrastructure, and release processes)Found that engineers were spending 30% of their time on operational overhead Once I had this data, I assembled a virtual team of 7-8 engineers who could spend some time fixing these issues. Within six months, we made the builds faster, made tests less flaky, and automated most of our manual release work. What started as just a feeling that things weren't working well turned into real improvements. Setting a Vision That Scales At one point, I noticed that several teams were independently solving the same problem in their own ways. I got all the tech leads together to come up with one solution that could work for everyone. Starting with a simple but aspirational goal — one common platform that all teams could use — we created a proposal that aligned with company objectives. After multiple iterations and stakeholder feedback, we refined this vision and eventually built a cross-team platform that not only saved engineering hours but also enabled better collaboration. Keys to setting effective technical vision: Identify themes and recurring needs across teamsDocument clear goals and reasoningInclude stakeholders early for feedback and buy-inConnect technical initiatives to business impactContinuously communicate progress and celebrate wins Growing Your Team Through Mentorship Great tech leads are force multipliers. Early in my leadership journey, I noticed a talented developer who had great ideas but hesitated to voice them. I started regular 1:1 chats, creating a safe space for discussion. We brainstormed on design discussions and potential new projects, and I encouraged him to lead design reviews. Within a few months, he was confidently handling complex features and even mentoring others. The time invested in his growth was worth it: he became a go-to person on the team, and I could delegate more, knowing he would deliver. To mentor effectively: Schedule regular check-ins focused on growth and feedbackLead by example in code reviews and design discussionsGive others stretch assignments with your supportShare knowledge openly through talks and documentation Practical Tips for New Tech Leads Start Small and Build Up First, become a mentor to other engineers before taking on leadership.Make connections with people in other teams.Share what you know through proper documentation and team sessions. Drive Business Impact Understand what matters for your product's success.Show how technical decisions affect business goals.Focus on what actually helps users and brings value. Focus on Enabling Others Give team members chances to grow.Document and share knowledge properly.Celebrate when the team does well (not just your own work). Handle Unclear Situations Break big, vague problems into smaller, clear tasks.Prototype things before building a large solution.Be ready to change plans when you get new information. Conclusion Moving from engineer to tech lead really pushes you out of your comfort zone. You'll still be solving problems and building things, but now you do it by focusing on making your team successful, building good relationships with everyone, and connecting technical work to business goals. Being a good technical leader means making an impact that goes way beyond just writing code. More

Generative AI in Agile: A Strategic Career Decision

By Stefan Wolpers

CORE

TL;DR: A Harvard Study of Procter and Gamble Shows the Way Recent research shows AI isn’t just another tool — it’s a “cybernetic teammate” that enhances agile work. A Harvard Business School study of 776 professionals found individuals using AI matched the performance of human teams, broke down expertise silos, and experienced more positive emotions during work. For agile practitioners, the choice isn’t between humans or AI but between being AI-augmented or falling behind those who are. The cost of experimentation is low; the potential career advantage, on the other side, is substantial. A reason to embrace generative AI in Agile? Introduction: The AI Dilemma in the Agile Community Interestingly, Agile practitioners are no strangers to skepticism about new tools. The Agile Manifesto’s emphasis on “individuals and interactions over processes and tools” has led some to dismiss generative AI (GenAI) as another buzzword that distracts from human-centric collaboration. Others fear it might worsen an already challenging job market. But what if avoiding AI is the riskier choice? The job market concerns are multifaceted and reflect broader anxieties about AI’s impact on knowledge work. Many Agile practitioners worry that AI could automate core aspects of their roles — from documentation and facilitation to coaching and analysis. In a profession already experiencing market fluctuations due to economic uncertainty and evolving organizational models, the prospect of AI-driven efficiency creates fear that fewer Agile professionals will be needed. Some practitioners also believe that organizations might reduce investment in human Agile talent or consolidate roles if AI can generate user stories, facilitate retrospectives, or analyze team metrics. These concerns are particularly acute for practitioners who have positioned themselves primarily as process experts rather than as the strategic business partners they are supposed to be. (Remember: We are not paid to practice [insert your Agile framework of choice] but to solve our customers’ problems within the given constraints while contributing to the organization’s sustainability.) Drawing parallels to the Y2K crisis — where preparation, not panic, averted disaster — adopting GenAI today represents a low-cost, high-upside strategy for Agile professionals. Early adopters will thrive if AI becomes foundational to work (as the Internet did). If not, the cost of learning is negligible. The evidence, however, increasingly points toward AI’s transformative potential. Rather than relying on theoretical arguments or anecdotal evidence alone, we can turn to rigorous research that directly examines AI’s impact on collaborative work. One particularly relevant study provides empirical insights into exactly how AI affects the kind of cross-functional collaboration at the heart of Agile practice. Generative AI in Agile Analysis: The Harvard Business School P&G Study The 2025 Harvard Business School study “The Cybernetic Teammate” by Dell’Acqua et al. provides compelling evidence of AI’s impact on collaborative work. Conducted with 776 professionals at Procter & Gamble, this large-scale field experiment examined how AI transforms three core pillars of collaboration: performance, expertise sharing, and social engagement. The study implemented a 2×2 experimental design where participants were randomly assigned to work either with or without AI, and either individually or in two-person teams on real product innovation challenges. This design allowed researchers to isolate AI’s specific effects on individual and team performance. The results were striking. Individuals with AI-matched teams’ performance without AI, suggesting that AI can effectively replicate certain benefits of human collaboration. Furthermore, AI broke down functional silos between R&D and Commercial professionals, with AI-augmented individuals producing more balanced solutions regardless of their professional background. Perhaps most surprisingly, the study found that AI’s language-based interface prompted more positive emotional responses among participants, suggesting it can fulfill part of the social and motivational role traditionally offered by human teammates. These findings directly address the concerns of Agile practitioners and provide empirical evidence that AI adoption can enhance rather than detract from the core values of Agile work. Debunking Myths: AI vs. Agile Values Myth 1: “AI Undermines Agile’s Focus on Human Interaction” This objection misunderstands both the Agile principle and AI’s role. The principle doesn’t reject tools; it prioritizes human connections while acknowledging that appropriate tools enable better interactions. The Dell’Acqua study demonstrates that AI-enabled individuals matched the performance of human teams in innovation tasks. Crucially, AI didn’t replace collaboration — it enhanced it. The P&G study also revealed that participants reported more positive emotions (excitement, energy) and fewer negative ones (anxiety, frustration) when using AI, mirroring the social benefits of teamwork. The researchers concluded that AI can “fulfill part of the social and motivational role traditionally offered by human teammates.” (Source: Abstract, page 2.) For Agile practitioners, this suggests that AI automates administrative work while potentially enhancing the emotional experience of the work itself. Myth 2: “AI Will Replace Agile Roles” The more realistic concern isn’t replacement by AI itself, but competition from AI-augmented practitioners. The P&G study found that when some professionals leverage AI to accomplish in minutes what takes others hours, the performance differential becomes significant. The research conclusively showed that AI doesn’t eliminate expertise — it redistributes it. In the P&G context, R&D professionals using AI proposed more commercially viable ideas, while Commercial professionals delivered solutions with greater technical depth. As the study authors noted, “AI breaks down functional silos” (Source: Abstract, page 2), allowing professionals to exceed their traditional domain boundaries. For Agile practitioners, this implies that AI won’t eliminate the need for facilitation, coaching, or product ownership — but it will transform how these roles operate, requiring practitioners to adapt their skills accordingly. Myth 3: “Using AI Is ‘Cheating'” This perspective assumes a static definition of human capability that never reflects reality. Knowledge workers have always integrated tools — from calculators to spreadsheets to project management software — to enhance their capabilities. The P&G study reinforces this view, showing that AI represents a continuation of this tradition, not a departure from it. The Y2K Parallel: Preparing for Uncertainty Like Y2K, no one knows if today’s AI hype will fizzle or redefine work. But consider: Cost of learning: Free and low-cost tools and 4-8 hours of practice can yield initial competence,Upside: Early adopters gain a significant career edge if AI becomes essential. The P&G study provides concrete evidence of these benefits, showing that AI users completed tasks 12–16% faster while producing higher-quality results. As the researchers noted, “Individuals with AI produced solutions at a quality level comparable to two-person teams” (Source: 1. Introduction, page 4), demonstrating substantial productivity gains with relatively minimal learning investment. Practical Applications for Generative AI in Agile Workflows Here’s how Agile practitioners can apply lessons from the P&G study to their workflows: 1. Enhancing Agile Events Sprint Planning Generate draft acceptance criteria for complex user storiesIdentify potential dependencies or risks in planned workSuggest task breakdowns for large epics The P&G study found that “AI can enhance collaborative performance by automating certain tasks and broadening the range of expertise available to team members” (Source: 2. Related Literature, page 7). In Sprint Planning, this translates to faster generation of comprehensive acceptance criteria, which teams can then review, refine, and customize based on domain knowledge — improving thoroughness while reducing time spent by up to 70%. (Note: You can also repeat these benefits during refinement sessions.) Retrospectives The study revealed that “GenAI’s ability to engage in natural language dialogue enables it to participate in the kind of open-ended, contextual interactions that characterize effective teamwork” (Source: 2. Related Literature, page 7). For retrospectives, AI can analyze previous retrospective notes to identify recurring patterns, generate discussion questions based on Sprint metrics, and suggest innovative formats tailored to specific team challenges. 2. Documentation and Communication User Story Refinement The P&G research demonstrated that AI-assisted participants produced “substantially longer outputs”(Source: 5.1 Performance, page 15) in less time than control groups. For Agile teams, this efficiency translates directly to user story refinement: Before: ‘Create a dashboard for the sales team.’After AI refinement: ‘As a sales director, I want a customizable dashboard showing region-specific sales metrics with trend visualization to quickly identify performance gaps and opportunities without manually compiling data. The dashboard should update in real-time, allow filtering by product line and period, and highlight variances against targets.’ Stakeholder Communications The study found that AI enables participants to “breach typical functional boundaries” (Source: 1. Introduction, page 4), allowing them to communicate more effectively across different domains of expertise. For Agile practitioners, AI can help convert technical updates into business-focused language, generate executive summaries of Sprint outcomes, and create tailored communications for different stakeholder groups. 3. Conflict Resolution The P&G experiment showed that “AI may also enhance collaborative team dynamics and transform the division of labor” (Source: 2. Related Literature, page 7). For conflict resolution, this suggests using AI to simulate stakeholder negotiations where it role-plays as a resistant Product Owner, helping facilitators practice persuasion techniques. This provides a safe environment for practitioners to hone their facilitation skills before high-stakes conversations. (Note: Try Grok to train arguing.) 4. Meeting Facilitation Given the finding that “the adoption of AI also broadens the user’s reach in areas outside their core expertise” (Source: 1. Introduction, page 4), meeting facilitation becomes another area ripe for AI enhancement. Practitioners can use AI to generate structured agendas, provide real-time suggestions for redirecting off-track discussions, and create comprehensive summaries and action items from meeting notes. Measurable Benefits of Generative AI in Agile The P&G study quantified several benefits of AI-augmented Agile practices that directly apply: Time Efficiency: The study found that AI-enabled participants completed tasks 12-16% faster than those without AI. For Agile practitioners, this could translate to a 40-60% reduction in time spent on routine documentation.Quality Improvements: Individuals with AI showed a 0.37 standard deviation increase in quality over the control group, comparable to the quality improvement seen in two-person teams (0.24 standard deviation). This suggests a potential 35% increase in clarity and completeness for Agile artifacts.Strategic Focus: The time saved allows practitioners to redirect 2-3 additional hours per week toward strategic activities and direct team support.Team Satisfaction: The study’s finding that “professionals reported more positive emotions and fewer negative emotions when engaging with AI” (Source: 1. Introduction, page 5) suggests that AI can reduce frustration with administrative tasks, potentially improving team health metrics. Current Limitations and When Not to Use Generative AI in Agile Despite its benefits, the P&G study acknowledges that AI has limitations. As the researchers note, “Our findings suggest that adopting AI in knowledge work involves more than simply adding another tool” (Source: 1. Introduction, page 5). For Agile practitioners, understanding these limitations is crucial. AI cannot replace: Building genuine human connections and psychological safety,Understanding team dynamics and interpersonal tensions,Making value judgments that require organizational context,Facilitating difficult conversations and conflict resolution. The P&G experiment involved “one-day virtual collaborations that did not fully capture the day-to-day complexities of team interactions in organizations — such as extended coordination challenges and iterative rework cycles” (Source: 7. Discussion and Conclusion, page 20). Thus, Agile practitioners should be aware of these limitations when implementing AI in ongoing team dynamics. Warning signs that you should revert to fully human approaches: When the AI’s suggestions lack the necessary organizational context,For highly sensitive situations involving personal conflicts,When the team needs emotional support rather than logical solutions,When building trust is the primary objective. Generative AI in Agile: Enhancing Collaboration, Not Replacing It The P&G study conclusively shows that AI enhances team collaboration rather than diminishing it. The researchers observed that AI served as a “boundary-spanning mechanism, helping professionals reason across traditional domain boundaries and approach problems more holistically” (Source: 7. Discussion and Conclusion, page 21). For Agile teams, this translates to several collaboration enhancements: Meeting preparation. AI can help team members organize thoughts and contributions before meetings, leading to more productive discussions where everyone participates meaningfully.Knowledge gap bridging. The study found that “workers without deep product development experience can leverage AI’s suggestions to bridge gaps in knowledge or domain understanding” (Source: 1. Introduction, page 4). This allows less experienced team members to contribute more confidently in discussions.Idea expansion. Dell’Acqua et al. observed that “individuals using AI achieved similar levels of solution balance on their own, effectively replicating the knowledge integration typically achieved through team collaboration” (Source: 7. Discussion and Conclusion, page 21). This helps teams break out of groupthink and consider a wider solution space.Documentation burden reduction. The study showed that AI-enabled participants produced significantly longer, more detailed outputs in less time. When one person volunteers to document decisions, AI can help them create comprehensive notes quickly, avoiding the common pattern where documentation responsibilities limit active participation. Getting Started: Simple Steps When using generative AI in Agile, start with simple steps: Experiment with prompts. Use role-based framing (“Act as a Scrum Master…”) and constraints (“Propose solutions under a 2-week deadline”). The P&G study found that participants who interacted more extensively with AI through iterative prompting achieved better results.Focus on repetitive tasks. Automate standup summaries, reports, or OKR tracking. The research shows that AI provides the greatest benefits for routine, structured tasks where domain expertise can be clearly communicated.Iterate. The study demonstrated that treating AI as a collaborative partner rather than a mere tool yielded superior results. Approach AI like a junior teammate — critique its outputs, refine prompts, and integrate feedback. Common Pitfalls to Avoid When using generative AI in Agile, avoid the following anti-patterns: Excessive secrecy. The P&G researchers found that transparency about AI usage was important for team dynamics. Being secretive can create distrust; be appropriately transparent.Over-reliance. The study noted that AI sometimes produced hallucinations, highlighting the need to maintain critical thinking and not accept AI outputs without verification.Tool fixation. Dell’Acqua and others emphasized focusing on outcomes rather than becoming obsessed with the tools themselves.Ignoring team dynamics. Consider how AI adoption affects team interactions and relationships, a concern highlighted by the researchers when discussing the social implications of AI integration. Conclusion: Evolve Agility, Don’t Betray It The P&G study provides compelling evidence that AI acts as a “cybernetic teammate,” augmenting human skills, not replacing them. As Dell’Acqua et al. conclude, “By enhancing performance, bridging functional expertise, and reshaping collaboration patterns, GenAI prompts a rethinking of how organizations structure teams and individual roles” (Source: 1. Introduction, page 5). Agile’s strength lies in adaptability, and dismissing AI contradicts that principle. By embracing AI now, practitioners future-proof their careers while staying true to Agile’s core mission: delivering value faster, together. The cost of waiting is your competitive edge. Start small, experiment often, and let AI handle the mundane so you can focus on the meaningful. The most Agile approach to AI isn’t blind enthusiasm or stubborn resistance — it’s thoughtful exploration, validated learning, and continuous adaptation based on results. This approach has guided Agile practitioners through previous technological shifts and will serve you well in navigating this one. Have you started embracing AI in your daily work? Please share with us in the comments. More

How Agile Outsourcing Accelerates Software Project Delivery

By Michael Chukwube

Is Vibe Coding Agile or Merely a Hype?

By Stefan Wolpers

CORE

AI-Driven Kubernetes Troubleshooting With DeepSeek and k8sgpt

By Rajesh Gheware

CORE

AI in Agile Product Teams

I have been interested in how artificial intelligence as an emerging technology may shape our work since the advent of ChatGPT; see my various articles on the topic. As you may imagine, when OpenAI’s Deep Research became available to me last week, I had to test-drive it. I asked it to investigate how AI-driven approaches enable agile product teams to gain deeper customer insights and deliver more innovative solutions. The results were enlightening, and I’m excited to share both my experience with this research approach and the key insights that emerged. Working With Deep Research: A New Level of Analysis My experience with Deep Research was remarkably productive. After providing a detailed research prompt exploring how AI transforms agile product development, I received a comprehensive synthesis that went far beyond what I expected from market research handled by an AI agent. What impressed me most was how the research agent engaged with my initial request, asking clarifying questions about industry focus, development stages, timeframes, and company sizes of interest. This collaborative refinement process ensured the final report addressed my specific needs rather than delivering generic information. (Other agents are less inclined to do so; Perplexity or Grok, for example.) Within just 11 minutes, Deep Research compiled findings from 16 sources into a cohesive narrative featuring three in-depth case studies and a thoughtful cross-case analysis. The analysis didn’t just aggregate information — it extracted meaningful patterns and presented actionable insights in a structured, easily digestible format. (Download the complete report here: AI in Agile Product Teams: Insights from Deep Research and What It Means for Your Practice.) Three Illuminating Case Studies The report examined how diverse organizations leveraged AI within their agile frameworks to transform product discovery and delivery: Lightful: Agile “AI Squad” Powers Nonprofit Communication This London-based tech company formed a cross-functional “AI Squad” with designers, engineers, and product managers working in daily iterations. Rather than adopting AI for its own sake, they identified specific pain points for their nonprofit clients and experimented with AI solutions in short, rapid cycles. Their most successful innovation was an “AI Feedback” tool that helps nonprofit users improve social media posts by providing suggestions with explanations. The solution educated users while augmenting (not replacing) human creativity. The team’s agile approach allowed them to quickly adapt when new AI models became available, swapping in improved technology within Sprints. PepsiCo: AI Uncovers the “Perfect Cheetos” PepsiCo employed generative AI and deep reinforcement learning to experiment with Cheetos’ shape and flavor. First, they built a digital simulation of the production process. Then, they trained an AI system to optimize variables like dough moisture, temperature, and machine settings — running thousands of virtual trials far faster than physical lab tests could allow. The AI-designed “perfect Cheetos” drove a 15% increase in market penetration by aligning product attributes more closely with consumer preferences. PepsiCo combined human expertise with AI experimentation. Domain experts set clear objectives, while the AI explored the solution space extensively, identifying non-intuitive combinations that human R&D might have overlooked. Wayfair: Generative AI Enhances Customer Visualization Wayfair developed “Decorify,” an AI-powered interior design tool that lets shoppers upload photos of their room and describe a desired style. The generative model produces a photorealistic image of the space filled with Wayfair furniture and decor matching that style, with products linked for purchase. Within months of launch, the tool had generated over 175,000 room designs for users. It addressed a critical customer need: helping me envision what furniture would look like in my space. Wayfair treated this as an MVP: launching early, then improving through iterative updates based on user feedback and usage data. Six Key Patterns for Success Across these case studies, Deep Research identified recurring patterns that contributed to successful AI integration within agile frameworks. As the report concluded: “Common threads in our case studies include a relentless focus on customer needs, iterative development to harness AI’s fast improvements, cross-functional teamwork, and careful attention to ethics and data quality.” The six key patterns worth highlighting are: 1. AI as an Insight Engine, Not Just an Efficiency Tool In all three cases, AI revealed deeper customer insights that shaped product direction — from identifying content quality needs at Lightful, discovering precise product traits consumers love at PepsiCo and revealing style preferences at Wayfair. Organizations leveraged AI to uncover latent needs and patterns, not just to automate existing processes. 2. Customer-Centric, Problem-First Approach Successful teams started with customer problems and needs, then applied AI as appropriate — not vice versa. This discipline prevented wasted effort on “cool” AI ideas that don’t move the needle. The question was always: “How can AI help solve this specific customer problem?” rather than “Where can we use AI?” 3. Agile Methods Amplify AI’s Impact (and Vice Versa) The fast pace of AI advancement requires the adaptability that agile practices provide. Teams integrated AI work into their work cadence: using short experiments to test viability, Sprints to build AI-driven features incrementally, and frequent reviews to assess outcome quality with stakeholders. This created a powerful feedback loop where Agile’s adaptability enabled quick AI piloting, and AI-generated insights informed subsequent iterations. 4. Cross-Functional Teams and Skills Are Essential AI projects intersect with data science, engineering, design, and domain expertise. The most successful implementations involved diverse teams with a shared language around AI. This prevented miscommunication and unrealistic expectations, allowing for smoother collaboration and more effective solutions. 5. Human Oversight, Ethics, and Data Quality Teams created processes to verify AI outputs and mitigate errors or bias. This included adding QA steps in the definition of done, A/B testing AI decisions against human ones before full rollout, and proactively addressing ethical considerations. Transparency with users and ensuring regulatory compliance were essential. 6. Leadership Buy-In and Culture of Experimentation Leadership support provided vision and resources, empowering teams to iterate without fear. Setting realistic expectations — not overhyping AI as magic but as a powerful tool requiring refinement — and communicating progress in terms leadership cares about (customer metrics, ROI, competitive advantage) were crucial. Becoming Obsolete Is a Choice, Not Inevitable What strikes me most about these findings is how they challenge the fear narrative around AI for knowledge workers. Many professionals view AI as a threat rather than a paradigm-shifting technology like the printing press, electricity, or the Internet. Yet, these case studies tell a different story. In each example, human expertise remained essential. AI enhanced human capabilities rather than replacing them. At Lightful, the AI provided suggestions but kept humans in the creative loop. At PepsiCo, domain experts set objectives and guided the AI’s exploration. At Wayfair, the AI visualization tool helped customers make better decisions but didn’t replace the human shopping experience. These observations suggest that becoming obsolete in the age of AI is a choice, not an inevitability. The practitioners who thrive will be those who learn to leverage AI as a collaborator — using it to uncover insights from unstructured data, simulate complex scenarios, and enhance their decision-making. What This Means For Your Agile Practice As agile practitioners, we’re uniquely positioned to embrace AI. The agile mindset — focused on adaptation, continuous improvement, and delivering customer value — aligns perfectly with the evolving nature of AI technology. Here are three takeaways for your own practice: Start small, learn fast. Begin with specific customer pain points where AI might offer value. Run experiments in short iterations, gather feedback, and adapt quickly.Build cross-functional AI literacy. Ensure your team has a shared understanding of AI capabilities and limitations. “Understanding” doesn’t mean everyone should become a data scientist, but everyone should understand enough to collaborate effectively.Keep the human at the center. Design AI implementations that augment human creativity and decision-making rather than attempting to replace it. Most applications keep humans in the loop. Many agile teams are currently missing opportunities to leverage AI for deeper insights — particularly in transforming qualitative data from user research, retrospectives, and customer feedback into actionable patterns. There’s enormous untapped potential in using AI to extract meaning from the rich but unstructured data we already collect. Conclusion The future belongs to agile practitioners who can pair human judgment with AI’s analytical power. We can deliver unprecedented value to our customers and organizations by embracing this partnership rather than fearing it. Deep Research is merely a glimpse into this future. Have you experimented with AI in your agile practice? What opportunities do you see for AI to enhance rather than replace your team’s capabilities? How might we ensure that AI serves our agile values rather than undermining them? Please drop me a line or comment below.

By Stefan Wolpers

CORE

The Tree of DevEx: Branching Out and Growing the Developer Experience [Infographic]

Editor's Note: The following is an infographic written for and published in DZone's 2025 Trend Report, Developer Experience: The Coalescence of Developer Productivity, Process Satisfaction, and Platform Engineering. Engineering teams are recognizing the importance of developer experience (DevEx) and going beyond DevOps tooling to improve workflows, invest in infrastructure, and advocate for developers' needs. By prioritizing things such as internal developer platforms, process automation, platform engineering, and feedback loops, organizations can remove friction from development workflows, and developers gain more control over their systems, teams, and processes. According to recent research: 44% have adopted platform engineering practices and/or strategies67% are satisfied or very satisfied with their org's continued learning opportunities43% use workflow and/or process automation in their org26% of respondent orgs use an internal developer platform72% prefer to collaborate via instant messaging, with sprint planning in second place (59%)40% of respondent orgs conduct dev advocacy programs and/or initiatives By focusing on developer productivity, infrastructure, and process satisfaction, teams can foster an environment where developers can do their best work. This infographic illustrates the strategies shaping DevEx and how developers and organizations are adapting to improve efficiency and innovation. This is an excerpt from DZone's 2025 Trend Report, Developer Experience: The Coalescence of Developer Productivity, Process Satisfaction, and Platform Engineering. Read the Free Report

By DZone Editorial

Psychological Safety as a Competitive Edge

Psychological safety isn’t about fluffy “niceness” — it is the foundation of agile teams that innovate, adapt, and deliver. When teams fearlessly debate ideas, admit mistakes, challenge norms, and find ways to make progress, they can outperform most competitors. Yet, many organizations knowingly or unknowingly sabotage psychological safety — a short-sighted and dangerous attitude in a time when knowledge is no longer the moat it used to be. Read on to learn how to keep your competitive edge. The Misinterpretation of Psychological Safety I’ve noticed a troubling trend: While “psychological safety” is increasingly embraced as an idea, it is widely misunderstood. Too often, it is conflated with comfort, an always-pleasant environment where hard conversations are avoided and consensus is prized over candor. This confusion isn’t just conceptually muddy; it actively undermines the very benefits that psychological safety is meant to enable. So, let’s set the record straight. Actual psychological safety is not about putting artificial harmony over healthy conflict. It is not a “feel-good” abstraction or a license for unfiltered venting. At its core, psychological safety means creating an environment of mutual trust and respect that enables candid communication, calculated risk-taking, and the open sharing of ideas — even and especially when those ideas challenge the status quo. (There is a reason why three out of five Scrum Values — openness, respect, and courage — foster an environment where psychological safety flourishes.) When Amy Edmondson of Harvard first introduced the term, she defined it as a “shared belief held by members of a team that the team is safe for interpersonal risk-taking.” Digging deeper, she clarified that psychological safety is about giving candid feedback, openly admitting mistakes, and learning from each other. Note the key elements here: candor, risk-taking, and learning. Psychological safety doesn’t mean we shy away from hard truths or sweep tensions under the rug. Instead, it gives us the relational foundation to surface those tensions and transform them into growth. It is the baseline of trust that allows us to be vulnerable with each other and do our best work together. When teams misunderstand psychological safety, they tend to fall into one of two dysfunctional patterns: Artificial harmony. Conflict is avoided at all costs. Dissenting opinions are softened or withheld to maintain an illusion of agreement. On the surface, things seem rosy – but underneath, resentments fester, mediocre ideas slip through unchecked, and the elephants in the room live happily ever after.False bravado. The team mistakes psychological safety for an excuse for unfiltered “brutal honesty.” Extroverts voice critiques without care for their impact, bullying the introverts, thus eroding the trust and mutual respect that proper psychological safety depends on. Both failure modes arise from the same fundamental misunderstanding: psychological safety prioritizes comfort over candor or honesty over care. In reality, true psychological safety dismisses these false dilemmas. It involves discovering how to engage in direct, even challenging, conversations in a way that enhances rather than undermines relationships and trust. Psychological Safety and Radical Candor This is where the concept of “radical candor” comes in. Coined by Kim Scott, radical candor means giving frank, actionable feedback while showing that you care about the person on the receiving end. It is a way of marrying honesty and empathy, recognizing that truly constructive truthtelling requires a bedrock of interpersonal trust. This combination of directness and care is at the heart of psychological safety, and it is utterly essential for agile teams. Agile’s promise of responsiveness to change, creative problem-solving, and harnessing collective intelligence depends on team members’ willingness to speak up, take smart risks, and challenge established ways of thinking. This requires an environment where people feel safe not just supporting each other but productively sparring. Consider the Daily Scrum or stand-up, a hallmark Agile event. The whole point is for team members to surface obstacles, ask for help, and realign around shifting goals. But that is hard to do if people feel pressured to “seem fine” or avoid rocking the boat. Actual psychological safety creates space for people to say, “I’m stuck and need help,” “I don’t know,” or “I disagree with that approach” without fear of judgment or retribution. Or take the Retrospective, which is also dedicated to surfacing and learning from failure. (Of course, we also learn from successes.) If people think that talking openly about mistakes will be held against them, they’ll naturally ignore, massage, or sanitize what happened. (This is also the main reason a team should not include members with a reporting hierarchy between them.) Psychological safety shifts that calculus. It says, “We’re in this together, win or lose,” which paradoxically gives teams the courage to scrutinize their losses more rigorously to learn from failure. Zoom out, and you’ll see psychological safety running like a golden thread through all the core Agile principles: “individuals and interactions over processes and tools,” “customer collaboration over contract negotiation,” and “responding to change over following a plan.” Enacting these values in the wild requires team environments of enormous interpersonal trust and openness. That is the singular work of psychological safety — and it is not about being “soft” or avoiding hard things — quite the opposite. (Think Scrum Values; see above). The research shows that psychological safety isn’t just a kumbaya aspiration — it is a performance multiplier. Google’s comprehensive Project Aristotle, which studied hundreds of teams, found that psychological safety was the single most significant predictor of team effectiveness. Teams with high psychological safety consistently delivered superior results, learned faster, and navigated change more nimbly. They also tended to have more fun in the process. Moreover, teams with high psychological safety are more likely to create value for customers, contribute to the bottom line, retain top talent, and generate breakthrough innovations — the ultimate competitive advantage. In other words, psychological safety isn’t a nice-to-have; it is a strategic necessity and a profitable asset. So, how do we cultivate authentic psychological safety in our teams? A few key practices: Frame the work as learning. Position every project as an experiment and every failure as vital data. Publicly celebrate smart risks, regardless of the outcome. Make it explicit that missteps aren’t just tolerated—they’re eagerly mined for gold.Model fallibility. As a leader, openly acknowledge your own mistakes and growth edges. Share stories of times you messed up and what you learned. Demonstrating vulnerability is a powerful signal that it is safe for others to let their guards down, too. (Failure nights are a great way of spreading this message.)Ritualize reflection. Take Retrospectives seriously to candidly reflect on what’s working and what’s not. Using structured prompts and protocols helps equalize airtime so that all voices are heard (Think, for example, of Liberating Structures’ Conversation Café). The more habitual reflection becomes, the more psychological safety will deepen. If necessary, consider employing anonymous surveys to give everyone a voice.Teach tactful candor. Train the team in frameworks for giving constructive feedback, such as the SBI (situation-behavior-impact) model or non-violent communication. Emphasize that delivering hard truths with clarity and care is the ultimate sign of respect — for the individual and the shared work.Make space for being a mensch. Kickoff meetings with quick personal check-ins. Encourage people to bring their whole messy, wonderful selves to work. Share gratitude, crack jokes, and celebrate the small wins. Psychological safety isn’t sterile; it is liberatingly human. Most importantly, recognize that building and sustaining psychological safety is an ongoing practice — not a one-and-done box to check. It requires a daily recommitment to choosing courage over comfort, purpose over posturing, and the hard and necessary truths over the easy fake-outs. Like any meaningful discipline, it is not always comfortable. Working and relating in a psychologically safe way can sometimes feel bumpy and exposing. We may give clumsy feedback, stumble into miscommunications and hurt feelings, and face hard facts we’d rather avoid. But that is the point: genuine psychological safety transforms uncomfortable moments from threats into opportunities. It allows us to keep showing up and learning together, especially when we feel most vulnerable. It fosters a team culture that is resilient enough to endure the necessary friction of honest collaboration and turns them into something impactful and clarifying. That is the promise of psychological safety. More than just another buzzword or checklist item, it is about cultivating the soil for enduringly healthy and productive human relationships at work. It is about creating the conditions that support us in growing into them together. Put simply, without psychological safety, Agile can’t deliver on its potential. With psychological safety, Agile can indeed come alive as a force for creativity, innovation, and, yes, joy at work. Conclusion Start by looking honestly at your team: How safe do people feel taking risks and telling hard truths? What is the one conversation, the one elephant in the room, you have been avoiding that might unlock the next level of performance and trust? Challenge yourself to initiate that talk next week — and watch the ripple effects unfold. Embracing this authentic version of psychological safety won’t be a walk in the park. You and your team will face uncomfortable moments of friction and vulnerability. Team members may drop out, feeling too stressed about it. But leaning into that discomfort is precisely how you will unleash your true potential. Psychological safety is about building a resilient team to navigate tough challenges and have difficult conversations because you know you have each other’s backs. That foundation will allow you to embrace agility as it is meant to be.

By Stefan Wolpers

CORE

How to Grow Fast From an Intern to Senior Software Engineer

Thousands of new software engineers enter the industry every year with aspirations to make a mark, but many struggle to grow efficiently. Transitioning from an entry-level engineer to a senior software engineer is challenging and rewarding, requiring strategic effort, persistence, and the ability to learn from every experience. This article outlines a simple, effective strategy to accelerate your journey. This is not a shortcut; it is quite the opposite. This is a way to develop a solid base of earned knowledge for long-term growth with urgency and focus. Fast growth from intern to senior engineer requires a clear understanding of the expectations at each level, a focus on developing key skills, and a strategy that emphasizes both technical and interpersonal growth. This guide will help you navigate this journey effectively, laying out actionable advice and insights to fast-track your progress. Who Is This For? Before I go further, let me be more specific about the strategy's applicability in this article. This article concerns growing as a software engineer from an intern/new grad (L3 at Google or E3 at Meta) to a senior software engineer (L5 at Google or E5 at Meta). While the personal growth part of this article is generally applicable, the career growth part applies only to companies where the engineering career ladder is heavily influenced by prominent Silicon Valley companies such as Google Meta. Executing this strategy requires a healthy ambition with a matching work ethic. Key Differences Between Junior and Senior Engineers With that preamble, let's dive in. The first step is to understand what sets the two career levels apart. Once the difference is laid out, I will boil it down to a few dimensions of growth and demonstrate how the strategy covers all of them. While every organization defines these roles slightly differently, the expectations from these roles are roughly as follows: Entry-Level Software Engineer Works on small, well-defined tasks under close supervision.Relies heavily on existing frameworks, tools, and guidance from senior colleagues.Contributes individual pieces to a larger project, often with a limited understanding of the bigger picture. Senior Software Engineer Independently solves open-ended problems that impact a business domainThey are the domain experts. Owns large-scale projects or critical subsystems, taking responsibility for their design, development, and delivery.Designs complex systems, makes high-level architectural decisions, and anticipates long-term technical implications.Leads cross-functional efforts — partners across teams and functions to drive strategic initiatives that align with organizational goals.Maintains systems, leads incidents, and reduces toil. Participates in long-term planning. Mentors junior engineers. What Are the Areas of Growth? You need to grow in the following areas to close the gap from an L3 to an L5. 1. Independence L5 are independent agents. They are responsible for figuring out what to do, how to do it, who to talk to, etc. Ultimately, they must be able to deliver results on medium-sized projects without handholding. An L5 must be agentic, i.e., they should be able to provide value for at least a quarter in their manager's absence. 2. Functional Expertise L5 can be independent only when they have the required expertise. This has three dimensions: L5 must have technical and functional competence in all things related to your team. L5 must understand the business context in which their team exists and why it matters to users. They must have organizational know-how and social capital that enables them to work on cross-team projects. 3. Working With Others Given that L5s manage projects with significant complexity and scope, they must develop this meta-skill. This one has many dimensions, such as writing, communication, planning, project management, etc. This only comes with deliberate practice. 4. Leadership L5 leverages its expertise to make many small and big decisions. For more significant projects, you will need critical long-term thinking. You must uncover and present trade-offs and have strong opinions on what path the team should take. All of this is collectively covered under designing systems. This muscle, too, only comes with practice. Strategy: Spiral of Success “Take a simple idea, and take it seriously.” – Charlie Munger As you can see, there is much growing up to do. The overall strategy is simple: you need to maximize your learning rate. Learning in any complex domain happens only by doing, shipping, and learning from feedback. You need to do a lot of projects that are increasingly more complex. You need to optimize to be invited to do a lot of projects. For that, you need to increase the surface area of your opportunity. The best way to increase your opportunity surface area is to do the projects you get quickly and satisfactorily. Make doing things fast and well your brand. It is essential to go dapper in the significance of these two dimensions: Fast: This is the key to turbocharging our learning process. The quicker you complete an assignment, the faster you get to do the next one. This enables you to do newer, different things sooner. And you keep accumulating a ton of delivered impact. Well: Doing a project well is key to earning your team’s trust. This is the best signal that you are ready for more significant responsibilities. It tells the team that you can carry your weight and more. This leads to increased scope and complexity in the next project. A project not well done is worse than a project not done. It erodes your brand; it delays critical and complex projects getting your way, robbing you of opportunities. Doing the first 2-3 assignments really fast and well leads to new and slightly bigger projects coming your way. You repeat the same process, forming a virtuous growth cycle, and before you know it, you will become a crucial team member. With every project done You will learn new core skills, code something new, use new technology, see new interactions between technologies, and so on. You will become more independent and gain functional expertise. You will gain more business context, which you will be able to connect to previous learning. Your holistic mental map of the whole area will start to become richer. This will make you more mature in a domain and improve your intuition, thus making you more independent. You will earn the team's trust; you will make cross-team contacts. You keep accumulating social credentials. With time, you learn more about your adjacent teams, their systems, and how they join with your team. With more context, you become more creative, and you are able to generate more possible solutions to choose from. Once you have sufficient context, more open-ended assignments will come your way. You want to reach this phase as soon as you can. These projects give you the opportunity to hone skills like writing, designing, project management, cross-team work, and overall leadership. Notice that this strategy does not discuss what to do on the projects. It’s all about how to do them. The “what” will keep changing as you take on bigger responsibilities. Initially, this will primarily involve coding and testing. But increasingly, there will be more investigation, communication, coordination, design, and so on. The key point is that your strategy should be the same, and you should do every assignment very fast and very well. This means doing well and making fast changes with changes in scope. Just like writing good code has a learning curve, doing good planning or writing well will have a learning curve. The only way to learn fast is to lean in, go through the discomfort, and do a lot of it. You have to really earn it. Execution You can not apply this strategy blindly. You are providing a service. As you focus on learning and personal growth, ensure you deliver value to the organization and users. Ultimately, the delivered impact is the only thing that matters. While you're at it, treat everyone you interact with better than you wish to be treated. Be unreasonable in your hospitality. In the long run, this is in your self-interest because this leads to more opportunities coming your way. How to Do Things Fast? First, consider how much it should take you upfront for each granular task. And then see how much time it actually takes. This is not about being right; it is about being deliberate. You will be mostly wrong in both directions, and that's okay. This is about figuring out how and why you are wrong and incrementally correcting for it. If you finish too fast, you will adjust your intuition. If you finish too slowly, you need to debug why. From that debugging, you will learn to do things faster. It's a win-win situation. Projects can be done in a much shorter time than the time allocated to them, especially in cases where they don't need cross-team coordination. This is simply because people naturally procrastinate. To counter this, you should always set very aggressive timelines for yourself. You will meet such timelines more often than you think. An aggressive timeline helps attain focus, which is a force multiplier. You can do more in one 4-hour chunk than four 1-hour chunks. Find that sweet spot for you. Second, do not shy away from seeking help. Do not work in isolation. Sometimes, you spend a day on a thing that your teammate could have helped you within 15 minutes. Seek that help. Do your homework before seeking help. A well-framed help looks like this: I am trying to do X, I have attempted A, B, and C, and I am stuck. Can you help me figure out what I am missing? But seek the help. Because help means you will finish your assignment faster, and thus, you will be able to get the next assignment faster. Remember, your goal is to increase opportunity surface area. Finally, you must put in the hours — your growth compounds with time. Thus, the hours you put in the first few weeks, months, and years will keep giving benefits for many years to come. The intensity really matters here. With focus, a lot can be done in a day or a week. You could wrap your head around a new domain in a week. You could take a month, too, but then you lose out on some opportunities. Be relentless. How to Do Things Well? This boils down to two traits you can cultivate: curiosity and care. A healthy curiosity is essential to doing things well. It leads to more questions and a better understanding of the subject. Chasing every odd observation leads to the early identification of bugs and, thus, better quality output. With curiosity, you will not be confined to only your project. Still, you will learn from projects around you, too, which helps you spin up faster on the business domain and increases your opportunity surface area. Care is about the polish in your output. Early in your career, you still do not have a taste for what is considered “well done.” To counter that, you need to have a feedback and improvement loop. You will have teammates, users, or managers to work with for everything you work on. Show your work to them, seek feedback, identify improvement areas, and then make the improvements. Repeat this cycle many times, and fast. Naturally, you will develop a taste for what's well done, which is a requirement for a senior software engineer. What if Fast and Well Conflict? Suppose you have to make a tradeoff between well and fast. Prioritize doing things well. It's okay to be a bit slow initially while finding your feet, but it's never okay to half-do things. Here, seeking feedback is crucial, as your own sense of what is well done is not yet fully developed. Seek feedback on what you are good at and what is not overkill. Seek clarity on requirements. Get your plans reviewed by teammates just to make sure. A Few More Things About Day-to-Day Practices Take all assignments, even if you don’t like them. Growing as a senior means caring about the business. If there is something that you don't like to do but needs to be done to have a business outcome, take it. Volunteer for the things no one wants to do. Because you do things so fast, unpleasant assignments will be short-lasting and earn you a lot of goodwill. Some projects move slower than others despite your best efforts. But there will be downtimes. You will wait on other teams, data jobs will run long, builds will take time, code reviews will be delayed, and so on. To fully occupy yourself, try to have two or more assignments going in parallel, but ensure your primary assignments are always on track. If you still have spare time, read the incoming tickets, bug reports, and design documents. Keep up with the Slack discourse. Be a sponge, absorbing all the context around you. Curiosity will help here. Make a point of being excellent on on-call rotations, as they are an excellent learning opportunity to get involved in the whole team context. Tracking As you execute this strategy, it is essential to ensure that you are on the right track and hitting the milestones along the way. Phase 1: Foundations Complete small tasks. Build confidence by reliably delivering well-defined tasks.Understand team systems and tools. Gain familiarity with the team's codebase, tooling, and workflows.Build trust. As you deliver consistently, your team begins to rely on you for essential contributions.Grasp team dynamics. Develop an understanding of what everyone on the team is working on and how their work connects.Acquire operational knowledge. Achieve a working knowledge of the technologies and systems used by your team. Phase 2: Gaining Independence Write small design documents. Begin drafting plans for features that take several weeks to implement.Handle feedback. Your code and designs require minimal feedback as you consistently land on effective solutions.Communicate more. Transition from primarily coding to more discussions, planning, and presenting your ideas.Tackle cross-team investigations. Collaborate with other teams to solve problems that extend beyond your immediate scope.Lead incident responses. Take charge of resolving incidents and develop a reputation for reliability under pressure. Phase 3: Expanding Scope Own medium-sized projects. Successfully deliver projects lasting 2-3 months, taking responsibility for their end-to-end execution.Increase visibility. Participate in multiple discussions and contribute to projects spanning different teams.Propose solutions. Take on open-ended, high-level challenges and provide thoughtful, actionable solutions.Mentor peers. Offer guidance and support to less experienced colleagues, building your leadership skills.Contribute to design discussions. Meaningfully engage in conversations about system architecture and project strategies. Phase 4: Strategic Impact Lead large-scale projects. Identify and own initiatives that have cross-team or company-wide implications.Develop frameworks and tools. Create solutions that improve productivity or simplify workflows for your team.Advocate for best practices. Promote coding standards, testing strategies, and effective development processes.Represent your team. Act as a spokesperson in cross-functional meetings, advocating for your team’s goals.Drive innovation. Bubble ideas for what the team should tackle next and align them with organizational priorities. Tracking With Manager You need to make sure that your management team sees that you are progressing on the career ladder defined by the organization. You need to keep a tight loop with your manager and explicitly sync with them regularly. You could do this monthly at the completion of each project or both. The best way to keep yourself and your manager accountable is to document every such check-in in a structured way rooted in the ladder. Here is a template that could be useful for such tracking Axis of development Scope and Impact Functional Expertise Independence Working with others Leadership Jan 2025 Where do you think you stand Your self-review goes here Your self-review goes here Your self-review goes here Your self-review goes here Your self-review goes here Where does your manager think you stand Your manager’s review goes here Your manager’s review goes here Your manager’s review goes here Your manager’s review goes here Your manager’s review goes here Action Items Action items you both agree on Action items you both agree on Action items you both agree on Action items you both agree on Action items you both agree on Feb 2025 Where do you think you stand Where does your manager think you stand Action Items Project X Where do you think you stand Where does your manager think you stand Action Items Parting Thoughts Do not compare yourself with others. Focus on your rate of improvement. Your goal is to have the fastest possible growth for yourself, not to compare yourself to others. Everyone is different, with different starting points, in different teams, and with various projects. Thus, everyone’s growth will be different. Don't waste time agonizing over others.

By VIVEK YADAV

Driving Developer Advocacy and Satisfaction: Developer Experience Initiatives Need Developer Advocacy to Be Successful

Editor's Note: The following is an article written for and published in DZone's 2025 Trend Report, Developer Experience: The Coalescence of Developer Productivity, Process Satisfaction, and Platform Engineering. Developer experience has become a topic of interest in organizations over the last few years. In general, it is always nice to know that organizations are worrying about the experience of their employees, but in a market economy, there is probably more to it than just the goodwill of the C-suite. If we take a step back and consider that many organizations have come to terms with the importance of software for their business' success, it is clear that developers are critical employees not just for software companies but for every organization. It is as Satya Nadella famously stated: "Every company is now a software company." Improving the developer experience is then, as a result, making sure that the experience for developers is one that makes it easy to be productive and leaves the developer satisfied. There is a virtuous cycle between the ability to be productive, solve business problems, and derive satisfaction from a job well done. This is why so many organizations introduced developer experience initiatives, or "ways of working" workgroups, to fuel that virtuous cycle. There is a second consideration for developer experience: the world of technology has become faster and more complex. Where we had dozens of components that were released into production each quarter, we are now delivering hundreds or thousands of microservices multiple times per day. To make this possible, we have toolchains that can look as complex as the enterprise technology architecture, with dozens of products supporting every aspect of the technology delivery lifecycle. Developers, as a result, are often tasked with navigating the tooling landscape and the delivery processes that have evolved at the same speed as the enterprise tooling, leading to additional handovers, unnecessary system interactions, and wait cycles. This "toil" is not only reducing productivity, but it also impacts the satisfaction of the developer. One antidote to this is developer advocacy, which can be defined as a dedicated effort to channel the needs of developers to the right places in the organization to improve the developer experience. One last thing to touch on before diving into how to support developer advocacy in your organization is the rise of interest in development platforms. There are different names being used to describe similar concepts: platform engineering, internal developer platform, or engineering system. Combining developer advocacy with the implementation of such a platform provides a very concrete expression of aspects of the developer experience and can provide tangible measurements that can inform your advocacy efforts. Benefits of Developer Advocacy Lead to Improved Developer Experience Let's talk about benefits where it matters most: with your customers. To bring to life the quote about every company being a software company, imagine how customers experience your organization. Nowadays, that is most often through technology, which can take many forms: Most bank transactions are not actions in a physical branch with a person, but rather through mobile or internet banking.Tesla customers often consider the regular feature update as the most meaningful engagement with the Tesla company.Even retail shopping is now a technology experience either through self-checkout terminals, direct-toconsumer sales channels online, or large technology marketplaces like Google, Amazon, or Facebook. The people in your organization who shape those interactions are the developers. Bringing the developers closer to the customer, allowing them to focus on solving customer problems, and delighting them with good customer experiences are actions that drive revenue and profits for organizations. While this benefit is the most important, it is, however, also relatively hard to measure. Productivity measurements have been traditionally difficult to achieve in software development — attempts with function points, story points, or misguided attempts with lines of code have all been mostly abandoned. What we can measure, however, is the opposite of productivity: toil. Toil takes many forms and can be measured in many cases. Reasonable measures include: Cycle time for processesNumber of handoversNumber of systems one needs to engage with to achieve a certain technology processReworkAnd many others These measures can be modeled into financial benefits (such as reduction of cost) where necessary, or can just be used to guide the developer's advocacy efforts with a developer experience scorecard as seen in Figure 1. Figure 1. Developer experience scorecard There are other less measurable benefits that may be introduced through developer advocacy as well. Some of the challenges for developers may come from a sub-optimal architecture, which reduces the efficiency of getting things done. It is very likely that the same architectural challenges also affect the customer or your resiliency. Addressing this may uplift more than just the developer experience. The same is true for the process improvements driven by your developers, which may free up stakeholders in those processes to do other things as well as create an overall positive shift in the organizational culture. Culture in an organization, after all, is enacted through actions, and making those actions more positive and meaningful will positively influence the culture. Lastly, improving the developer experience goes hand in hand with an improvement of DevSecOps practices; this improves productivity, as highlighted above, but also improves your security posture and operational reliability, which in turn, improves the customer experience. This is another virtuous cycle we want to leverage. Figure 2. Developer experience virtuous cycle What Developer Advocacy Means in Practice Developer advocacy programs should cover four different areas that reinforce each other: engineering processes, engineering culture and career models, developer platforms and tools, and creating communities. Engineering Processes For developer advocacy to be a win-win for organizations and individuals, it has to find a way to make the right things easy to do. Improving efficiency opens up cost reductions and makes the employee more satisfied, and this requires process redesign work. Luckily, developers know how to improve algorithms, and deploying this skill to overall engineering processes can be a successful way to engage developers in redesigning the software engineering processes of an organization. Engineering Culture and Career Models Companies that now rely on software to be successful don't always have an engineering culture that supports the creative nature of software development. This is most clearly visible when there is no career model for people to progress outside of traditional people and business management. Progressing along technical excellence pathways requires new ways of evaluating performance and rewarding individuals. Developer Platforms and Tools Engineers gravitate to new tools, and while this should not be the sole focus of developer advocacy, supporting the improvements with the right tools and an intuitive developer platform goes a long way. Backstage is a popular open-source architecture for such a developer platform. The recent trend in popularity of topics related to platform engineering shows that the industry is investing in finding better ways to solve this. Creating Communities Advocacy requires support from the intended audience, which means developer advocacy needs to win the hearts and minds of the developers in the organization. One of the best ways to do this is to create a purpose broader than just the organization. We see this successfully at community events like devopsdays, Agile conferences, or technology conferences where people share their problems and solution approaches to further the engineering "craft." Figure 3. The pillars of developer advocacy Unfortunately, the implementation of each developer advocacy program differs as each company, their processes, and their technology are different. Therefore, it is important to use feedback loops to find out what works and what doesn't work. You can leverage the measures of the scorecard and/or direct feedback from the developer community to inform the next iterative evolution of your program. Don't just follow what other companies do; let yourself be inspired by them and chart your own course instead. Challenges for Developer Advocacy There are challenges for successful developer advocacy programs. The first one is the diversity of the audience: You likely deal with junior developers and veterans alike, developers working with technologies ranging from modern microservices over packaged software all the way to heritage mainframe software, and stakeholders who are either intimate with technology or have never written a line of code. Bringing all these people together requires building community, focusing on objective outcomes, and making advocacy an inclusive endeavor. Developer advocacy is not something that can be driven top-down; rather, it needs to be rooted in the community. Once you have the developer community in the organization behind you, you need to also have something in it for the executive ranks who need to keep funding this work. This ideally means finding tangible financial benefits in either cost reduction or increasing revenue; if that is not possible, an alternative is to at least show measurable positive customer impact. Following the earlier advice of making progress measurable will go a long way in keeping all stakeholders supportive. Conclusion From our discussion, it is clear that improving the developer experience and satisfaction should be at the top of technology executives' minds. One of the best ways to do that is by having a developer advocacy program that combines the soft aspects like developer career paths and encouraging an engineering culture with hard technology solutions like building a developer platform that makes engineering tasks easier to achieve. To keep the executive ranks supportive of your developer advocacy program, it is important to keep measuring progress and to be able to translate that progress into business measures, as we described in this article. Last but not least — this should be a little fun, too — give your developer platform an interesting name, create some gamification elements to encourage positive behavior, and build a community that cares for each other. Happy employees often create the best results, after all! This is an excerpt from DZone's 2025 Trend Report, Developer Experience: The Coalescence of Developer Productivity, Process Satisfaction, and Platform Engineering. Read the Free Report

By Mirco Hering

CORE

Redefining Developer Productivity: Balancing Data and Human Experience in Developer Success Measurements

Editor's Note: The following is an article written for and published in DZone's 2025 Trend Report, Developer Experience: The Coalescence of Developer Productivity, Process Satisfaction, and Platform Engineering. Delivering production-ready software tools requires focusing on developer productivity measured by qualitative and quantitative metrics. To understand developer productivity, this article focuses on three elements: Developers — the heart of productivityProcesses — enabling flow and efficiency in any developer productivity frameworkTechnology — including tools and measurements that can be used to track progress on productivity over some time Most developer productivity frameworks and tools focus only on data metrics to measure whether the team and individuals are productive, leaving the "why" and "how" questions unanswered. Furthermore, the link between individual developer productivity and team developer productivity is also often missed or ignored. Understanding the why and how of developer productivity is critical to identifying trends and proposing solutions and best practices. Why Is Developer Productivity Important? Developer productivity has a direct bearing on an organization's ability to deliver software on time, innovate, and sustain a competitive advantage. It also drives adoption and expansion, which ultimately benefits not only developers but businesses too. In addition to delivering roadmaps, developer productivity involves many aspects that affect developers' career progression, happiness, and skills development. Organizations can create positive initiatives under people's teams, and businesses thrive by focusing on processes, technology, and developers. This includes establishing new programs to enhance developers' lifestyles, continuous learning, and psychological safety. The efforts to measure developer productivity have evolved and advanced over the past decade. Organizations track performance with sophisticated tools (e.g., internal developer platforms, engineering intelligence platforms, and CI/CD and code analysis tools), and the SPACE model provides a holistic way to evaluate and understand developer productivity through satisfaction, performance, communication, and efficiency. The intended tracking of the SPACE model varies: Individuals — well-being, code quality, etc.Teams — collaboration, cycle time, and deployment efficiencyOrganizations — business objectives, engineering impact, etc. See Table 1 for further details: Individual OutcomesTeam OutcomesBusiness OutcomesRegular checks highlight areas for growth and improvementsInsights into workflows enhance team collaborationIdentifying gaps and accelerating time to market lead to faster deliveryDevelopers are more engaged when their contributions are recognized and rewardedAddressing imbalances prevents burnout and maintains moraleInsights into best practices can improve code quality and reduce the time to fix bugsStructured feedback aids in honing technical and soft skillsShared productivity metrics align team goals and foster transparencyEfficient processes minimize resource wastage and avoid bottlenecks Table 1. The importance of measuring developer productivity While all these results matter, the priority depends on the situation: For example, if the burnout is high, it is important to focus on welfare, whereas if the deployment is slow, efficiency and flow should take precedence. By continuously refining the metrics, organizations promote a culture of recognition, engagement, and innovation. Output vs Outcomes: Why Lines of Code or Tasks Completed Aren't Enough When measuring developer productivity, focusing completely on output — such as the lines of the code written or work — can be misleading. High output does not necessarily mean high effects; more code can introduce complexity, increase technical debt, or even slow down growth. Instead, organizations should prioritize outcomes, which measure the genuine distributed value, such as better user experience, fewer bugs, rapid deployment, or enhanced system reliability. A developer who writes low code but optimizes performance or automatic processes contributes more than one outcome that meets a high number of low-effect tasks. Qualitative and Quantitative Measurements for Developer Productivity Success Measuring developer productivity comes with many challenges. A major issue is an excessive addition to output-based metrics, such as code or functions, that can easily lead to misinterpretations and create competitive behavior. In addition, when a lot of emphasis is given to personal performance, it can overrun the importance of the team's cooperation and dynamics, eventually damaging the team's coordination. To get an accurate picture of productivity, it is necessary to balance both personal and team matrices, cooperation, and long-term success goals. Frequent tool, process, and business priority changes also cloud assessments of developer productivity. This explains why combining qualitative and quantitative approaches helps form a better understanding of developer productivity through measurable and subjective outputs. These methods enable organizations to strike a balance between efficiency and human-centered considerations, with the latter being entirely focused on the relationship between people, processes, and technology. Yet, while technological advancements offer deeper insights, challenges remain in ensuring these metrics capture the nuanced realities of software development. MeasurementsAdvantagesLimitationsQuantitativeOutput-based metrics: Lines of code, commits, or pull requestsObjective insights: Quantitative data provides clear, measurable benchmarksContextual gaps: Metrics may not reflect the complexity or impact of tasksEfficiency metrics: Time spent on tasks or resolving issuesScalability: Metrics can be applied across large teams or organizationsPotential for misinterpretation: Overemphasis on metrics can encourage gaming or unhealthy competitionDelivery metrics: Cycle time, lead time, and deployment frequencyTrend identification: Data reveals patterns and areas for improvementNot seeing the bigger picture: Data alone often overlooks collaboration, creativity, and other intangible factorsQualitativeSurveys and self-assessments: Gathering developer feedback on satisfaction and perceived productivityContextual depth: Qualitative methods capture the nuances of developer experiencesSubjectivity: Results can vary based on individual perspectives and biasesPerformance reviews: Evaluations based on observations and contextual understandingHuman focus: These approaches emphasize well-being and job satisfactionTime and effort: Collecting and analyzing qualitative data requires significant effort360 feedback: Insights from colleagues about contributions and collaborationHolistic insights: Qualitative data complements quantitative metrics for a more comprehensive viewReplication challenges: These methods may be harder to implement across large teams Table 2. Qualitative and quantitative approaches to measure developer productivity Combining Quantitative and Qualitative Measurements The combination of quantitative metrics and qualitative assessments generally achieves better outcomes. An organization that tracks deployment frequency but also engages in regular developer surveys is equipped with both performance data and team morale insights. While quantitative data — such as the purpose frequency, lead time, and error rate — provide qualitative insights via developer surveys, retrospectives, and user response, quantitative data alone is not enough. The combination ensures technical efficiency and human factors, such as morale, satisfaction, and workflow challenges, to create a more comprehensive understanding. Why mix these metrics? Relying completely on one type of measurement presents significant blind spots. Quantitative data can expose what is happening, but it often fails to explain why some trends emerge. For example, an increase in certification frequency can indicate efficiency benefits, but without qualitative input, you may ignore the rise of developer burnout due to the growing charge. Similarly, qualitative insights alone can provide valuable references, but there is a lack of an average indicator to track improvement over time. A well-balanced approach prevents misinterpretation and ensures that data-informed decisions are human-centered. However, this isn't a straightforward process. We face three major challenges: Integration of quantitative and qualitative metricsRisk of over-prioritizing either quantitative or qualitative measures, which could hinder the complementary benefits of the otherTailoring quantitative or qualitative measures to individual teams/projects To help combat these challenges, a solution that is permanent and outcome-focused is needed to ensure the success of combining metric types. Below is a five-step framework that can be used to overcome challenges while also increasing developer efficiency and productivity: Define a metric for measuring developer productivityCreate a feedback loop by reviewing metrics regularly and adapting based on developer feedbackPrioritize value over output by focusing on outcomes rather than outputsEnsure metrics support alignment of organizational goals and business objectivesEnable developers, automate workflows, and invest in the right developer productivity tools Figure 1. Key elements of developer productivity: developers, processes, and technology Conclusion Redefining developer productivity requires adopting a more balanced approach to traditional metrics, through which developers, processes, and technology are viewed both quantitatively and qualitatively. To give the correct value of productivity, organizations need to embrace both types of metrics. At the same time, organizations may need to invest in systems that align productivity measures with broader organizational goals. Finally, organizations should prefer collaboration, feedback loops, and results to promote the culture of continuous improvement without reducing productivity. Future efforts for developer productivity should be focused on increasing developer productivity platforms that integrate both qualitative and quantitative data. This involves the adoption of a balanced productivity assessment to keep pace with the development of the developer and the changing nature of technology. Embracing these strategies is an important step toward reshaping the perception of productivity, ensuring that it benefits developers, teams, and companies equally. This is an excerpt from DZone's 2025 Trend Report, Developer Experience: The Coalescence of Developer Productivity, Process Satisfaction, and Platform Engineering. Read the Free Report

By Fawaz Ghali, PhD

CORE

Why Platform Engineering Is Essential to DevEx: Understand the Relationship Between Platform Engineering and the Developer Experience

Editor's Note: The following is an article written for and published in DZone's 2025 Trend Report, Developer Experience: The Coalescence of Developer Productivity, Process Satisfaction, and Platform Engineering. Software engineering continues to be an exciting journey, where use cases not only feed creativity but also allow the underlying intellectual property to thrive. The biggest challenge is to make sure engineers maintain a focus on what allows their solutions to be differentiators in the market. That's where concepts like developer experience (DevEx) and platform engineering come into play. Let's explore how DevEx blazes the path for engineers to maintain control of their responsibilities and understand the impact of artificial intelligence (AI) and automation. Along the way, we'll define platform engineering and walk through an example implementation. Finally, we'll demonstrate the expected results of improved focus on architecture, productivity, and engineer satisfaction. Developer Experience According to Gwen Davis, DevEx is defined as: "...the systems, technology, process, and culture that influence the effectiveness of software development. It looks at all components of a developer's ecosystem — from environment to workflows to tools — and asks how they are contributing to developer productivity, satisfaction, and operational impact." The role of the software engineer continues to gain complexity as underlying business objectives mature. It's important to maintain a solid DevEx posture to help individual contributors remain satisfied and challenged… with the right problems to solve. From a tooling perspective, items like autocompletion, AI-driven suggestions, and static code analysis have improved the process of writing code. Tools like Renovate and Watchtower automate the process of dependency management and container base image updates. There are even pull request (PR) bots, which can do things like auto-approve touch-commit PRs, avoiding the assistance from another engineer. There are the social aspects of DevEx that should be kept in mind. Here, focusing on toil reduction is key, i.e. eliminating manual or repetitive tasks that bog down productivity. Making sure engineers have the ability to collaborate with others — especially across team boundaries — often avoids solving problems that already have a proven solution. Where possible, the use of metrics should be implemented and reviewed at least once during a development iteration (or sprint). Some key metrics that relate to DevEx include: Cycle time, the amount of time required to complete a task, keeping in mind the expected complexityPR lead time, the amount of time required to review, approve, and merge codeDeployment frequency, how often code is being deployed to productionMean time to recovery, the amount of time required to resolve an unexpected incident or failureChange failure rate, the percentage of changes that result in an unexpected incident or failure With these metrics in place, dashboards can be created, allowing teams to monitor their own metrics while also understanding metrics for all engineers. This can provide early indicators for when DevEx levels are trending low, which could be driven by changes being made that are outside an engineer's control. Platform Engineering According to a DZone Refcard, platform engineering is defined as: "...a discipline focused on creating and managing development platforms that significantly enhance software delivery processes. At its core, platform engineering aims to establish secure environments, automated and self-service tools, and streamlined workflows that empower development teams to write, test, and deploy applications effectively and consistently across various settings without worrying about operational complexities." In the DevEx section, I mentioned the importance of making sure that software engineers are focused on solving the right problems. Before the concept of platform engineering, individual teams were forced to focus time on solving common problems, like CI/CD and security, in a vacuum. Not only did this take away from their ability to enhance their products or services, but it also required teams to create and support tooling that is common to all teams. In fact, a given team's solution might miss auditing, compliance, and security requirements — leading to further refactoring down the road, such as unexpected penalties or fines. Platform engineering provides benefits across the entire development lifecycle for engineering teams. Three examples can be found with tooling, continuous integration, and continuous deployment. Tooling Automation Describing an organization's teams, services, and dependencies is the first step in allowing further automation to succeed. This metadata can be used to grant security and authorization for each team's source code repositories and solutions. The repository would introduce all aspects for each team. Every service would be described as well, linking the service to the team and any cloud-based functionality required by the service. This would eventually pave the way for a given software engineer to accomplish the following tasks: Introduce future code repositories owned by the team (services, infrastructure as code, etc.)Create and maintain the necessary infrastructure required to meet new use casesAccess and support services and dependencies by logging in with their standard credentials Continuous Integration Automation Taking continuous integration (CI) off the list of responsibilities for engineering teams is another key aspect of platform engineering. This removes the risk of a build-your-own DevOps approach and allows engineers to be focused on solving the right problems. A proper CI implementation would automatically launch the CI process each time a PR is created. Once the PR is created, the following required tasks could be initiated and managed by platform engineering: Perform static/code quality analysis, making sure corporate standards are being metValidate that zero, high, or critical vulnerabilities are being introduced as a result of the changesUpdate all base images when container images existAutomate signed deployment artifacts Additionally, when PRs are merged to the primary branch, the same tasks would be executed again, validating that the code being merged also maintains the same level of compliance. Continuous Delivery Automation It is important for platform engineering to assume responsibility for continuous deployment (CD) to standardize how deployments work across all individual teams. Doing so can eliminate the common toil-based task of making sure the engineer who modified the code is not the person deploying the code. By taking this approach, platform engineering's control over CD can introduce additional benefits. Here are some examples: Health-mediated deployments allow the deployment process to integrate with the observability platform, therefore halting deployments that impact established service metric thresholds.Integration with change management and application lifecycle management solutions tie deployments to the tickets related to a release and launch approval workflows when management approval is required.Rules can be introduced to make sure teams are adhering to software development lifecycle (SDLC) standards, like making sure a given deployment is deployed in all expected lower environments before being deployed to production. By using platform engineering for the separate CI and CD processes, teams can be assured that the code or container being deployed is the same signed image as the lower environments. DevEx and Platform Engineering Together Now that we have a strong understanding of DevEx and platform engineering, let's understand the impact these two concepts have when working together. DevEx is the more mature concept and is something software engineers have embraced for quite some time, long before the term was introduced. Engineering managers and directors have long wanted to understand the productivity across teams, leading to metrics being established. What wasn't known at the time was the strong relationship to job satisfaction levels. Platform engineering is certainly a corporate investment, which has its own budget expectations. Once corporations see the benefit of platform engineering, doors will open to implement the necessary tooling and CI/CD improvements. Once teams have onboarded to platform engineering, the following benefits will be realized by the entire organization: Reduced risk from an audit, compliance, and security perspectives due to a standardized and centralized SDLCDeployments less prone to issues related to inadequate code coverageDeployments free from significant vulnerabilities or code quality issuesImproved metrics: Cycle time improves due to engineers being focused on solving the right problem PR lead time increases to allow reviewers to focus on proposed changes Deployment frequency improves due to automation and platform engineering tooling Mean time to recovery improves due to engineering focus from tooling and automation Change failure rate improves due to the automation and platform engineering tooling, plus the benefits from engineers being focused on solving the right problemImproved job satisfaction levels due to software engineers being laser-focused on the role they accepted While it is possible to implement one concept without another, platform engineering is key to maximizing DevEx. Conclusion This exploration demonstrated how DevEx tooling removes tedious tasks from daily work and reduces toil along the way. The ability for teams to collaborate is key to understanding the lessons learned by others. Finally, the use of metrics can help individuals, teams, and organizations as a whole gain insight into key performance indicators. Platform engineering allows engineers to focus on solving problems and enhancing products by centralizing aspects common to all teams. In doing so, the wonderful side effects of auditing, compliance, and security concerns can be kept in check. When DevEx and platform engineering are combined, engineers are able to keep up with established objectives, focusing on solving the right problems and remaining engaged — all leading to increased job satisfaction. I can personally attest to these results as I've been working in this utopian environment for the last two years. My readers may recall my personal mission statement, which I feel can apply to any IT professional: "Focus your time on delivering features/functionality that extends the value of your intellectual property. Leverage frameworks, products, and services for everything else." The use of platform engineering is an example of how individual teams can leverage a common solution to allow them to extend the value of the products and services they own. DevEx ultimately measures the job satisfaction for individual contributors and their ability to work on the right tasks. As you reflect on this mission statement, consider how you would answer the following questions: Is your organization adhering to this mission statement?What is the overall impact by not adhering?How could adhering to this mission statement impact your bottom line? Have a really great day! Additional resources: "Developer experience: What is it and why should you care?" by Gwen DavisPlatform Engineering Essentials by Apostolos Giannakidis and Kellyn Gorman, DZone RefcardMend Renovate CLIWatchtower — a process for automating Docker container base image updates This is an excerpt from DZone's 2025 Trend Report, Developer Experience: The Coalescence of Developer Productivity, Process Satisfaction, and Platform Engineering. Read the Free Report

By John Vester

CORE

How to Integrate Platform Engineering Into Your Business

Editor's Note: The following is an article written for and published in DZone's 2025 Trend Report, Developer Experience: The Coalescence of Developer Productivity, Process Satisfaction, and Platform Engineering. How do we even start approaching platform engineering? The good news is that major organizations that have successfully adopted platform engineering have contributed their insights, best practices, and lessons learned to frameworks like the Cloud Native Computing Foundation's (CNCF) Platform Maturity Model and Microsoft's Platform Engineering Capability Model. These models provide a structured pathway for organizations to evaluate their current state and identify gaps and actionable steps toward building an effective internal developer platform (IDP). By following the practices of these models, you can create a roadmap for your platform engineering journey, starting with small, impactful improvements that gradually drive adoption across your organization, resulting in a unified and optimized platform. The following is an actionable checklist designed to guide the initial steps of integrating platform engineering into your business. Note that this checklist should not be treated dogmatically but rather as a flexible starting point to define your approach. 1. Ensure Change Readiness and Cultural Alignment Platform engineering is not only about technology; to be successful in your platform engineering journey, it is critical to prioritize people, processes, and culture alongside technology: Foster a culture of collaboration, open communication, and adaptability within the organization Instate change management strategies to address resistance and ease transitions Actively encourage experimentation and foster an environment where teams learn and adapt Communicate a compelling vision for platform engineering that aligns with the organization's values, processes, and tools 2. Gain Organizational Buy-In Getting buy-in from stakeholders and teams can be challenging, especially for large projects or when shifting strategies significantly. Focus on developing compelling strategies that align with your audience's motivations and goals: Identify key stakeholders (developers, operations, management, security, etc.); understand their priorities and concerns Align the platform engineering initiative with the identified priorities [For executives] Emphasize business outcomes like product success and overall business growth via increased innovation, reduced time to market, and operational efficiency [For engineering teams] Highlight automated workflows and reduced tooling frustrations Use metrics to build your case, such as projected gains in deployment speed or reduced ticket volumes Present early success metrics (e.g., increased developer satisfaction, faster deployment cycles) and address any concerns transparently Create a value map connecting platform engineering actions (e.g., automating infra provisioning) to business outcomes Pilot a thin slice of the platform with a small team to demonstrate impact Actively collect feedback and communicate progress regularly with visual comparisons to keep stakeholders engaged and aligned 3. Assess Current State of DevOps Practices Insights into your DevOps practices not only help secure leadership buy-in but also serve as a foundation for developing a strategic platform engineering roadmap: Evaluate key areas like IaC, automation, developer self-service, and policy enforcement (i.e., assess whether your IaC is well-standardized and if developers can leverage automated workflows to provision resources) Identify bottlenecks, recurring pain points, and areas for improvement Use the CNCF Maturity Model to map your practices across its levels, identifying gaps such as siloed teams or manual workflows Pair this with quantifiable metrics like time to value, onboarding efficiency, and DORA metrics to measure inefficiencies and performance issues 4. Define Clear Objectives and Metrics Before diving into platform development, take a step back and define what success looks like for your organization: Set measurable goals for your platform at each stage of maturity (e.g., cutting deployment times, boosting developer satisfaction, enhancing system reliability)Align these goals with your business objectives to avoid wasting time and resourcesDefine achievable goals and set realistic expectationsFor every goal, establish clear metrics to track progress and enable data-driven decisions 5. Develop a Platform Strategy Developing a platform strategy requires careful planning with all key stakeholders. A successful strategy should: Clearly articulate the starting point, acknowledge and address potential challenges, and set realistic expectationsEstablish both short-term milestones and long-term goalsBe built upon a foundation of four key principles: productivity, quality, security, and efficiencyGo beyond simply defining what the platform should do; understand how it will achieve its goals and why these goals are important A fundamental principle in platform engineering is following a product-led approach that ensures that the platform is designed and evolved according to the needs of the development teams. This involves: Conducting brainstorming sessions with the key stakeholders; consider using brainstorming tools such as the Platform Journey MapConducting interviews and surveys with development teamsCreating feedback loopsCreating user personas and journey maps to encapsulate common scenariosEvolving the platform by adopting team interaction modes: close collaboration at the beginning, solution discovery, and X-as-a-Service It is important to remember that the platform strategy should be regularly reviewed and adjusted as the platform evolves and new requirements emerge. 6. Build a Dedicated Platform Team Without a dedicated platform team to develop and manage the internal developer platform, individual product delivery teams often end up creating their own platforms and pipelines, leading to duplication and inefficiencies. A dedicated platform team ensures a cohesive, unified platform infrastructure while supporting developers by utilizing its capabilities. This team treats the platform as a product, continuously refining and improving it to meet the evolving needs of its users. Steps include the following:Assemble a cross-functional team of mostly technical generalists, including expertise in infrastructure, automation, security, and software development Clearly define roles to focus on designing, maintaining, and iterating on the IDP, distinct from application development efforts Treat the platform as a product by conducting user research, gathering feedback, and refining features to meet developer needs Secure a dedicated budget and ensure the team has the tools, training, and cultural support needed to drive platform adoption Give a descriptive name to the team to distinguish from other product development teams, such as: Engineering Enablement Developer Experience Shared Tools Centre-of-Excellence 7. Adopt a Thin Platform Approach and Avoid Overengineering Adopting a thin platform approach ensures that your platform evolves organically while avoiding unnecessary complexity. This approach balances rapid adoption with long-term scalability and alignment to organizational goals: Build a minimum viable product (MVP) with only the essential services and capabilities needed to streamline repetitive development tasksFocus the MVP on simplicity, usability, and supporting a single "golden path" for consistent developer experiencesDesign the initial platform with basic resources and features that span the technical estate, avoiding overengineeringAvoid adding unnecessary features early on to prevent overwhelming users and complicating workflowsCreate a central catalog for all provisioned infrastructure and resources tied to golden paths to enable visibility and governanceEmbed security and compliance practices, such as Security as Code and Policy as Code, directly into the platform's design from the startShare an internal roadmap highlighting current platform value, future milestones, and goals to align organizational prioritiesRefine the platform in a Beta stage by testing foundational capabilities, improving quality, and productizing features for production useUse pilot user groups to test updates and new features in controlled environments to gather feedback and minimize disruptions before broader rolloutsApply the thinnest viable platform (TVP) mindset at every stage to focus on sustainable growth and avoid unnecessary complexity 8. Drive Platform Adoption Driving platform adoption requires more than just building a technically sound product — it demands cultivating trust, voluntary collaboration with platform champions, and open feedback channels with the dev teams and stakeholders: Launch a pilot program with a small group of enthusiastic developers to test the platform and provide actionable feedback Offer early adopters comprehensive training, clear documentation, and responsive support to quickly resolve issuesUse the pilot phase to refine the platform, address pain points, and build trust with usersCommunicate the platform’s value proposition through KPIs and practical examples that showcase simplified workflows, increased productivity, and faster value deliveryAssign a "platform champion" in each development team to advocate for the platform and demonstrate its time-saving and efficiency-boosting benefitsBuild developer trust by avoiding mandates to use the platform and, instead, foster voluntary engagement and collaborationRecognize that adoption is gradual and work closely with developers to encourage buy-in and commitmentMaintain open feedback channels like office hours, forums, or surveys to continuously gather insights from users and platform championsAct on user feedback to iteratively improve the platform and address developer concernsLeverage platform champions to share success stories and advocate for broader adoption within the organization 9. Measure and Iterate for Success Effective measurement and continuous iteration are the cornerstones of a successful platform engineering strategy, enabling organizations to align their platforms with evolving needs: Define actionable and reproducible KPIs tailored to your organization’s unique needs and platform objectives Measure success with KPIs like deployment frequency, change lead time, change failure rate, mean time to recovery (DORA metrics), developer satisfaction scores, platform adoption rates, and security compliance scoresUse tools like net promoter score (NPS) surveys to gauge developer sentiment and identify opportunities for improvementGather feedback regularly from developers and stakeholders to refine adoption strategies and address evolving needsCreate dashboards to visualize metrics, improve communication, and enhance transparency for all stakeholdersUse dashboards to monitor platform usage, pinpoint bottlenecks, and analyze developer interaction patterns for actionable insightsIncorporate advanced analytics to assess the platform's impact on business outcomes and support precise ROI calculationsLeverage predictive analytics to anticipate future platform needs, aligning development with usage trends and organizational goalsContinuously iterate on the platform based on insights from KPIs, feedback, and analytics to ensure it remains relevant and valuableShare progress and a data-driven roadmap with stakeholders to maintain alignment and build trust in the platform's value Conclusion As you embark on your platform engineering journey, remember there is no one-size-fits-all solution. Customize the approaches and strategies presented in this checklist to suit your organization's needs, and remain agile as both the platform and its requirements evolve. With a clear vision, leadership buy-in, change sponsors, a dedicated platform team, platform champions, voluntary developer engagement, open feedback channels, and a data-driven approach, you can build an IDP that delivers business value and increases innovation across your organization. This is an excerpt from DZone's 2025 Trend Report, Developer Experience: The Coalescence of Developer Productivity, Process Satisfaction, and Platform Engineering. Read the Free Report

By Apostolos Giannakidis

CORE

Integrating AI Agent Workflows in the SOC

Defending against zero- to low-cost attacks generated by threat actors (TA) is becoming increasingly complex as they leverage sophisticated generative AI-enabled infrastructure. TAs try to use AI tools in their attack planning to make social engineering schemes, convincing phishing emails, deepfake videos, different types of malware, and many other types of attack vectors. A potential solution to defend against these challenges is to enable the use of GenAI and AI agents in the Security Operations Center (SOC). An orchestrated workflow with a team of AI agents presents an opportunity for better response. In traditional detection and response, detections are not easily achieved, and manual responses cannot match the required machine-level speed. To avoid burnout and alert fatigue of SOC analysts, a shift in the SOC strategy is required by automating routine tasks using AI agents. What Is a SOC? A Cyber Security Operations Center (SOC) is a unit within an organization responsible for monitoring and responding to cyber threats in real time. The team of cybersecurity analysts operates 24/7 to investigate alerts, determine their severity, and take necessary actions. What Is an Alert and What Does a SOC Analyst Do in General? Detection logic triggers an "alert" when it meets specific thresholds or behaviors. A human analyst performs multiple decisions to respond to each alert with accuracy. Generally, working an alert involves developing context about the user involved, the activity concerned, the nature of the system involved, and a detailed investigation of what had happened. To deal with an alert, an analyst has to do many things, such as looking for strange things in network traffic, examining multiple processes and log sources, checking HTTP headers, sandboxing, decoding obfuscated scripts, scanning and isolating devices, blocking malicious indicators of compromise, quarantining malicious payloads, removing malicious emails from users' inboxes, collecting artifacts, resetting credentials, making reports, and more, depending on the situation. Analysts also use several tools and technologies like security information and event management (SIEM), security orchestration, automation, and response (SOAR), endpoint detection and response (EDR), open-source intelligence (OSINT), and many others to complete these tasks. If required, a SOC analyst is also responsible for involving multiple other teams, like cyber intelligence, threat hunting, legal, incident response and management, data assurance, network security, cloud security, identity and access management, and others. In addition, an analyst must also be on the lookout for any undetected suspicious events that were taking place during the corresponding timeframe of the alerted event. An analyst performs all these tasks in real time, often under immense pressure to ensure that no genuine positive events are missed. And yes, that is a lot. Fortunately, many of these manual and repetitive tasks can be automated by AI agents and workflow, making the job of an analyst more efficient and reducing response times. In many cases, integrated AI agents can handle alerts end-to-end, provided they have the required permission to make changes. Understanding Automation and Agents Every industry relies on some form of automation to offload simple and mechanical human labor. People heavily use robotic process automation (RPA) to automate repetitive tasks, and it excels when used with pre-defined rules. RPA cannot make new decisions, cannot learn from feedback, and needs constant human oversight. An automated system of multiple such agents is not flexible, and making changes to those systems can be tedious. These traditional automation Agents do not have any memory or feedback loop to evolve on their own and attempt to make human-like decisions. Understanding AI Agents The potentiality of LLMs extends beyond generating great stories and programs. AI agents and agentic AI systems use GenAI models and LLMs to autonomously perform tasks on behalf of end users. Think of AI agents as advanced software programs that can perform a task automatically, like traditional automation agents, and they can also tune their behavior dynamically. Interactions with these agents can be in the form of a natural language prompt (NLP) from a user or a function call from another agent. AI agents can tune their actions according to the new training data received in the form of feedback. In an LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several other tools, playbooks, and memory. For more robust agents, we must limit their scope. Instead of expecting a single agent to work autonomously, a system of AI agents can be used at scale to achieve a high level of intelligent automation that may gradually develop and replicate human-like decision-making abilities. Figure 1: AI agent AI agents operate in a continuous cycle where they take inputs and process them to take necessary actions. This cycle is continuous and allows the agent to change behavior dynamically as required. Rule-based systems, machine learning models, or decision trees can implement the agent's function. Below is a simplified overview of AI agents. There can be different variations and overlaps in these categories: Simple reflex agents. Act based on the current state of the environment. Respond to immediate conditions with predefined rules.Model-based reflex agents. Maintain an internal model of the environment. Consider past states to make decisions beyond immediate input.Goal-based agents. Have defined goals and evaluate actions based on progress towards those goals. Select actions that move them closer to their objectives.Utility-based agents. Optimize performance based on a utility function. Make decisions that maximize expected happiness or satisfaction.Learning agents. Improve performance by learning from experience. Adapt and refine their behavior over time. Concept of an AI Agent-Enabled Semi-Autonomous Cyber SOC To handle an alert, SOC analysts rely on the Standard Operating Procedure (SOP) or a playbook to ensure that nothing is missed while addressing a potential threat. SOPs and playbooks also ensure consistency among actions taken by different analysts. SOPs and playbooks are a set of repetitive tasks that can be automated. Static automation struggles to adjust to dynamic requirements and is difficult to modify. AI agents can solve this problem by adapting to new information and tweaking actions accordingly. Integrating these procedures as instructions to the agents can dramatically increase response speed and reduce human errors. Figure 2: Block diagram for a semi-autonomous SOC Descriptions of different AI agents can be found below: Data Ingestion and Enrichment Agents Alert Fetcher – Receives alerts from various security tools (SIEM, EDR, IDS/IPS, etc.)Alert Aggregator – Correlates, deduplicates, and prioritizes alertsEntity Extractor – Extracts key entities (IP addresses, user accounts, endpoints, etc.)Context Collector – Gathers relevant contextual information (user role, previous alerts, nature of endpoints, access levels, membership, file prevalence, etc.)Enrichment Agent – Enriches entities with threat intelligence and context Investigation and Analysis Agents Investigator Agent – Assigns an alert to the AI SOC analyst and attempts to develop a context based on the rules triggered. The agent evaluates responses from other AI agents.Evidence Collector – Collects and preserves relevant evidence for that alertEvidence Analyzer – Sand-boxing files, scanning URLs, decoding scripts, etc. Decision Making and Response Agents Action Determiner – Recommends appropriate actions (block, quarantine, isolate)Action Sequencing Agent – Determines the optimal order for executing actionsNote Maker Agent – Documents the incident investigation and response processDecision Maker Agent – Evaluates overall response strategyResponder Agent – Executes automated actions based on the sequenceEscalation Agent – Escalates critical incidents to human analysts Decision Making and Response Agents Action Determiner – Recommends appropriate actions (block, quarantine, isolate)Action Sequencing Agent – Determines the optimal order for executing actionsNote Maker Agent – Documents the incident investigation and response processDecision Maker Agent – Evaluates overall response strategyResponder Agent – Executes automated actions based on the sequenceEscalation Agent – Escalates critical incidents to human analysts Control and Coordination Agents Notification Agent – Notifies relevant personnel of critical alertsError Reporting Agent – Monitors and reports errors within the AI agents and integrationsVerification Agent – Monitors the executed actions and makes sure desired actions are completed Note: This is a simplified representation. Real-world implementations may involve more complex interactions and additional AI agents. Example of an Unwanted Blacklisted Software Found on a System In this simple hypothetical situation, a malicious tool named ThisIsBad.exe was found on a host called theonlyhost-win. The SIEM triggered an alert based on the executable's bad reputation for enumerating usernames on the device. In this case, the EDR policies were set to only Alert and not Block or Remove. The Alert Fetcher Agent receives the alert from SIEM, and the Entity Extractor Agent extracts key entities like user name theusualsuspect, hostname, and file name. The Enrichment Agent now enriches file information with threat intelligence, like reputation and behavior, via dynamic analysis information. The Investigator Agent assigns an alert to the AI SOC analyst and communicates with the Evidence Collector Agent and Evidence Analyzer Agent to preserve the file, run it in the sandbox, and grab detailed behavior. The Investigator Agent evaluates the results and notices suspicious behavior based on enumerating users logged in on the device based on the integrations and constant communications with a custom LLM trained to recognize suspicious patterns in the behavior of file execution. Decision-making and Response Agents collectively block the file, delete it, initiate a live scan on the device, make notes, and recommend policy changes to human analysts. The below figure gives an idea of how the AI agents will work together and take action at a very high speed. This representation may change in actual implementation and may involve more communications among the AI agents, LLM, custom playbooks, and all necessary sources of information. The AI agents will learn this behavior and automatically execute actions if the same file is spotted next time. This capability of an AI agent-enabled semi-autonomous SOC system, if implemented correctly, will save a lot of time and resources for the organization and strengthen the security posture. Figure 3: High-level view of AI agents handling an event Integrating AI Agents-Enabled Workflows in the SOC Ecosystem The true power of the above framework emerges when the AI agents are combined with other areas of SOC workflows. In the SOC, some important actions besides handling the alerts are manual, like sending emails, raising tickets with different teams, updating and closing the cases, logging them for future reference, etc. Creating a workflow allows AI agents to operate independently to some extent and continuously improve over time. Hyper-automation is required to handle real-world scenarios in the SOC. It is also crucial to balance autonomy with high precision by keeping a human in the loop. Recent advancements allow for the building of flexible workflows with native integrations across multiple platforms and products. The ability to quickly produce a sophisticated workflow for handling custom scenarios will be the key to this transition to a semi-autonomous SOC. As our reliance on AI-enabled hyper-automation increases, we will optimally leverage human expertise to design robust workflows capable of managing repetitive tasks. Challenges Models limit agents, so it's crucial to rigorously train and test them in a cybersecurity context.AI models perform best on data similar to their training data. Unfamiliar environments can significantly impact their effectiveness.AI models and AI agents themselves can be targets for attacks, compromising the system's effectiveness.The traceability of AI agents is also a key piece in ensuring that the SOC sees all the actions taken in case an incident needs to be revisited.We must thoroughly evaluate the risk of granting access to tools, user data, documents, SOPs, etc. In addition to the above challenges, organizations might face unknown challenges based on the current maturity level of their SOC capabilities and the resources involved in implementing the new approach.

By Keyur Rajyaguru

Implementing SOLID Principles in Android Development

Writing software is an act of creation, and Android development is no exception. It’s about more than just making something work. It’s about designing applications that can grow, adapt, and remain manageable over time. As an Android developer who has faced countless architectural challenges, I’ve discovered that adhering to the SOLID principles can transform even the most tangled codebases into clean systems. These are not abstract principles, but result-oriented and reproducible ways to write robust, scalable, and maintainable code. This article will provide insight into how SOLID principles can be applied to Android development through real-world examples, practical techniques, and experience from the Meta WhatsApp team. Understanding SOLID Principles The SOLID principles, proposed by Robert C. Martin, are five design principles for object-oriented programming that guarantee clean and efficient software architecture. Single Responsibility Principle (SRP). A class should have one and only one reason to change.Open/Closed Principle (OCP). Software entities should be open for extension but closed for modification.Liskov Substitution Principle (LSP). Subtypes must be substitutable for their base types.Interface Segregation Principle (ISP). Interfaces should be client-specific and not force the implementation of unused methods.Dependency Inversion Principle (DIP). High-level modules should depend on abstractions, not on low-level modules. Integrating these principles into Android development will allow us to create applications that are easier to scale, test, and maintain. Single Responsibility Principle (SRP): Streamlining Responsibilities The Single Responsibility Principle is the foundation of writing maintainable code. It states that each class must have a single concern it takes responsibility for. A common anti-pattern is considering activities or fragments to be some "God classes" that handle responsibilities starting from UI rendering, then data fetching, error handling, etc. This approach makes a test and maintenance nightmare. With the SRP, separate different concerns into different components: for example, in an app for news, create or read news. Java class NewsRepository { fun fetchNews(): List<News> { // Handles data fetching logic } } class NewsViewModel(private val newsRepository: NewsRepository) { fun loadNews(): LiveData<List<News>> { // Manages UI state and data flow } } class NewsActivity : AppCompatActivity() { // Handles only UI rendering } Every class has only one responsibility; hence, it’s easy to test and modify without having side effects. In modern Android development, SRP is mostly implemented along with the recommended architecture using Jetpack. For example, logic related to data manipulation logic might reside inside ViewModel, while the Activities or Fragments should just care about the UI and interactions. Data fetching might be delegated to some separate Repository, either from local databases like Room or network layers such as Retrofit. This reduces the risk of UI classes bloat, since each component gets only one responsibility. Simultaneously, your code will be much easier to test and support. Open/Closed Principle (OCP): Designing for Extension The Open/Closed Principle declares that a class should be opened for extension but not for modification. It is more reasonable for Android applications since they constantly upgrade and add new features. The best examples of how to use the OCP principle in Android applications are interfaces and abstract classes. For example: Java interface PaymentMethod { fun processPayment(amount: Double) } class CreditCardPayment : PaymentMethod { override fun processPayment(amount: Double) { // Implementation for credit card payments } } class PayPalPayment : PaymentMethod { override fun processPayment(amount: Double) { // Implementation for PayPal payments } } Adding new payment methods does not require changes to existing classes; it requires creating new classes. This is where the system becomes flexible and can be scaled. In applications created for Android devices, the Open/Closed Principle is pretty useful when it comes to feature toggles and configurations taken dynamically. For example, in case your app has an AnalyticsTracker base interface that reports events to different analytics services, Firebase and Mixpanel, and custom internal trackers, every new service can be added as a separate class without changes to the existing code. This keeps your analytics module open for extension — you can add new trackers — but closed for modification: you don’t rewrite existing classes every time you add a new service. Liskov Substitution Principle (LSP): Ensuring Interchangeability The Liskov Substitution Principle states that subclasses should be substitutable for their base classes, and the application’s behavior must not change. In Android, this principle is fundamental to designing reusable and predictable components. For example, a drawing app: Java abstract class Shape { abstract fun calculateArea(): Double } class Rectangle(private val width: Double, private val height: Double) : Shape() { override fun calculateArea() = width * height } class Circle(private val radius: Double) : Shape() { override fun calculateArea() = Math.PI * radius * radius } Both Rectangle and Circle can be replaced by any other one interchangeably without system failure, which means that the system is flexible and follows LSP. Consider Android’s RecyclerView.Adapter subclasses. Each subclass of the adapter extends from RecyclerView.Adapter<VH> and overrides core functions like onCreateViewHolder, onBindViewHolder, and getItemCount. The RecyclerView can use any subclass interchangeably as long as those methods are implemented correctly and do not break the functionality of your app. Here, the LSP is maintained, and your RecyclerView can be flexible to substitute any adapter subclass at will. Interface Segregation Principle (ISP): Lean and Focused Interfaces In larger applications, it is common to define interfaces with too much responsibility, especially around networking or data storage. Instead, break them into smaller, more targeted interfaces. For example, an ApiAuth interface responsible for user authentication endpoints should be different from an ApiPosts interface responsible for blog posts or social feed endpoints. This separation will prevent clients that need only the post-related methods from being forced to depend on and implement authentication calls, hence keeping your code and test coverage leaner. The Interface Segregation Principle means that instead of having big interfaces, several smaller, focused ones should be used. The principle prevents situations where classes implement unnecessary methods. For example, rather than having one big interface representing users’ actions, consider Kotlin code: Kotlin interface Authentication { fun login() fun logout() } interface ProfileManagement { fun updateProfile() fun deleteAccount() } Classes that implement these interfaces can focus only on the functionality they require, thus cleaning up the code and making it more maintainable. Dependency Inversion Principle (DIP): Abstracting Dependencies The Dependency Inversion Principle promotes decoupling by ensuring high-level modules depend on abstractions rather than concrete implementations. This principle perfectly aligns with Android’s modern development practices, especially with dependency injection frameworks like Dagger and Hilt. For example: Kotlin class UserRepository @Inject constructor(private val apiService: ApiService) { fun fetchUserData() { // Fetches user data from an abstraction } } Here, UserRepository depends on the abstraction ApiService, making it flexible and testable. This approach allows us to replace the implementation, such as using a mock service during testing. Frameworks such as Hilt, Dagger, and Koin facilitate dependency injection by providing a way to supply dependencies to Android components, eliminating the need to instantiate them directly. In a repository, for instance, instead of instantiating a Retrofit implementation, you will inject an abstraction, for example, an ApiService interface. That way, you could easily switch the network implementation, for instance, an in-memory mock service for local testing, and would not need to change anything in your repository code. In real-life applications, you can find that classes are annotated with @Inject or @Provides to provide these abstractions, hence making your app modular and test-friendly. Practical Benefits of SOLID Principles Adopting SOLID principles in Android development yields tangible benefits: Improved testability. Focused classes and interfaces make it easier to write unit tests.Enhanced maintainability. Clear separation of concerns simplifies debugging and updates.Scalability. Modular designs enable seamless feature additions.Collaboration. Well-structured code facilitates teamwork and reduces onboarding time for new developers.Performance optimization. Lean, efficient architectures minimize unnecessary processing and memory usage. Real-World Applications In feature-rich applications, such as e-commerce or social networking apps, the application of the SOLID principles can greatly reduce the risk of regressions every time a new feature or service is added. For example, if a new requirement requires an in-app purchase flow, you can introduce a separate module to implement the required interfaces (Payment, Analytics) without touching the existing modules. This kind of modular approach, driven by SOLID, allows your Android app to quickly adapt to market demands and keeps the codebase from turning into spaghetti over time. While working on a large project that requires many developers to collaborate, it is highly recommended to keep a complex codebase with SOLID principles. For example, separating data fetching, business logic, and UI handling in the chat module helped reduce the chance of regressions while scaling the code with new features. Likewise, the application of DIP was crucial to abstract network operations, hence being able to change with almost no disruption between network clients. Conclusion More than a theoretical guide, the principles of SOLID are actually the practical philosophy for creating resilient, adaptable, and maintainable software. In Android development, with requirements changing nearly as often as technologies are, embracing these principles will allow you to write better code and build applications that are a joy to develop, scale, and maintain.

By Farhana Haque