Methodologies

Modern authentication protocols, such as SAML and OpenID Connect (OIDC), rely heavily upon federation as the guiding principle to secure front-door authentication. Federation is an elegant approach for web-based applications to isolate authentication from the application using a trust established between the centralized identity provider (IDP) and a subscribing application. Armed with asymmetric key-based digital certificates, federation ensures that an application can securely leverage an external IDP service for authentication and free itself from the burden of handling user interaction during the authentication process. With federation comes the concept of single sign-on (SSO). Suppose the centralized IDP has already established a secure authentication session. In that case, multiple applications can benefit from being able to single sign-on and bypass redundant login, improving user experience and reducing authentication frictions. Limitation With Current Protocols However, federation exhibits limitations with modern applications, especially native mobile applications. Consider the scenario of an insurance provider's mobile application using biometric authentication with a central IDP. Once a user logs in to the application, she might want to go to a banking partner web application, for instance, by clicking a link inside the application. For a seamless user experience, the expectation is to transparently log the user in with SSO to the partner web application. The partner web application can be configured to use the same central IDP, but federation does not work since the mobile app does not have any easy way to share an existing IDP session with the web app. The de-facto technique for web SSO is using browser cookies, something that the native mobile app can not share with the mobile's system browser. As a result, the partner application that opens in the system browser does not know there has been an existing session, and SSO does not happen. Instead, the user would have to re-authenticate to the partner app. A potential solution is to use a shared identifier other than a browser cookie. That approach works well for SSO between mobile apps. Since both apps reside on the same device, they can securely share a session identifier using other forms of secure storage, such as the keychain in iOS devices. There is a standard specification for native-to-native SSO with OIDC. Unfortunately, there is no obvious way to implement SSO in a native-to-web scenario using industry standards. Often, individual applications resort to insecure ad hoc approaches. Identity Bridge Concept The identity bridge framework attempts to provide an architecture that applications can use to implement native-to-web SSO securely, staying close to the industry standard OIDC protocol. Since the protocol does not have a mechanism to support SSO out of the box, it needs a few additional constructs. First, the bridge relies on the central IDP's ability to delegate authentication to another IDP. This capability is known as inbound federation. Most commercial and open-source IDPs support inbound federation. Technically, communication between the two IDPs can use any federation protocol (SAML or OIDC) independently. However, the framework recommends OIDC since the industry is increasingly adopting it for new applications. Also, a service that we will call the Bridge service needs to be deployed. It will act as the inbound IDP communicating with the central IDP using OIDC. The bridge does not need a user interface; it will simply work as a proxy redeeming the mobile app authentication token for a web SSO session from the central IDP. A Model Bridge Framework Here is a model of the basic architecture for the bridge: There is a lot to unpack here: The user opens the mobile app and authenticates with the central IDP.Central IDP issues an authentication token to the application. For OIDC, the ID Token can be considered as the authentication token.The user then clicks on a web link (for the target web application) inside the mobile application.The web link opens in the system browser. The link also contains the authentication token as a parameter.The web application extracts the authentication token.The web app application initiates an OIDC authentication with the central IDP. First, it redirects to the IDP's /authorize endpoint. It also passes the authentication token in the login_hintparameter.The central IDP initiates another OIDC authentication flow, with the bridge acting as the inbound IDP. It passes on the login_hint parameter, which contains the authentication token from the mobile app.The bridge then generates a temporary authorization_code for one-time use and redirects back to the central IDP with the authorization code.The central IDP makes an HTTP POST call to the bridge's /token endpoint.The bridge validates the authentication token obtained from the mobile app passed through the login_hint parameter in the previous step. Using information from the source authentication token, it generates a new JWT token, signs it using its private key, and returns it to the central IDP.Next, the central IDP calls the keys endpoint of the bridge to obtain the public key to verify the signed JWT token.After the JWT is validated, the central IDP creates a web session and completes the authentication process by redirecting back to the web application. Security Considerations and Limitations Security Risks and Challenges Although the framework was designed using the standard OIDC protocol, it is not without its limitations. A proxy service must be deployed and maintained by adding an additional component to the solution. The vanilla architecture does not deal with many token security aspects. If additional measures are not taken with a leaked mobile authentication token, bypassing authentication in the web application is possible. Security Enhancements One implementation consideration for protecting the token is not using the original authentication token from the mobile application. Mobile applications tend to have long-lived tokens, and using them to perform SSO with a web application significantly increases the risk of session compromise. For example, the following strategy can be adopted: Before starting the web SSO, obtain a separately scoped ultra-short-lived ID token from the primary IDP. Use the new token to perform SSO instead of the original token for the mobile application. The ideal solution would be direct protocol support for native-to-web SSO. This would help avoid additional components and reduce the implementation complexity. Several promising works are underway to create an industry specification in this space. Implementation This section details a prototype implementation using Okta as the primary IDP. The bridge essentially acts as an OIDC server and implements three primary APIs. 1. /authorize This is the OIDC authorize endpoint. Okta will redirect to this endpoint using HTTP 302. The endpoint accepts the parameter login_hint, which carries the native token generated when the native app authenticates with Okta. For the prototype, the ID token issued by Okta is used as the native token. JavaScript app.get("/authorize", (request, response) => { customNonce = request.query.nonce; response.redirect( request.query.redirect_uri + "?code=" + request.query.login_hint + "&state=" + request.query.state ); }); Instead of generating a transient authorization code, the native token itself is passed back to the Okta redirect endpoint using the code parameter. Okta also generates a nonce value in the response. The nonce must later be included in the token. 2. /token OIDC token endpoint. Okta calls this endpoint to redeem the authorization code for a token. Communication occurs between Okta and the bridge. The resulting token is not reused in the application context. JavaScript app.post("/token", async (request, response) => { let originalClaims = jwt.decode(token); let claims = {}; claims.nonce = customNonce; claims.sub = originalClaims.sub; claims.ver = originalClaims.ver; claims.iss = originalClaims.iss; claims.aud = originalClaims.aud; claims.email = originalClaims.sub; customKeystore = jose.JWK.createKeyStore(); let result = await customKeystore.generate('RSA', 2048, {alg: 'RS256', use: 'sig' }); publicKey = result.toJSON(); privateKeyPEM = result.toPEM(true); publicKeyPEM = result.toPEM(false); customJWK= jwt.sign(claims,privateKeyPEM, { algorithm: 'RS256', header: { typ: 'jwt' } } ); var responseData = { access_token: customJWK, token_type: "Bearer", expires_in: 3600, scope: "openid", id_token: customJWK, }; response.send(responseData); }); In the prototype, the implementation effectively copies the claims of the native token to create a new JWT and includes the nonce value generated during the authorization step. It then signs and sends the token to Okta. To sign the token, it generates an ephemeral key pair for a one-time use. 3. /keys OIDC key endpoint. Okta uses this endpoint to fetch the public key of the bridge to verify the signed token issued at the /token endpoint. The implementation should return the ephemeral public key and then discard the keypair. JavaScript app.get("/keys", (request, response) => { let keys = {"keys":[publicKey]} response.send(JSON.stringify(keys)); }); A working prototype of the bridge service using Okta is available here. Sample applications for testing: Native app and Web app. Security Analysis The identity bridge is transparent to various applications. It communicates only with the primary IDP using OIDC, which is a secure federation protocol. The bridge must send a signed token to the IDP. It generates ephemeral key pairs for signing. Because every authentication is aided by a unique random keypair, a complex key management is not required. The bridge must ensure that the key pair is discarded after use or is in a failure condition. The authentication token used should have a sufficiently small value to reduce the potential for token leakage and guard against token replay attack. To further reduce the risk of token leakage, another idea is to use a very short duration token from the primary IDP generated just before initiating the SSO, instead of using the primary authentication token from the native application. Additionally, the bridge should be configured to accept requests only from whitelisted IPs related to the primary IDP. Real-World Use Cases Let us look at a few real-world scenarios in which native-to-web SSO flow is common. Corporate Portal A corporate mobile application can have links to authorized applications that are web-based and open in a system browser. After employees log into their corporate web portal, they typically single sign on to the applications they are authorized for. To provide a similar feature when they access the portal through the company's mobile application, a native-to-web SSO flow is required, particularly for web-based applications. Online Travel Agency The mobile app for an online travel agency can have web links to its partner airlines and hotels. Customers can then log in to the mobile app and click on their preferred airline or hotel to directly access and manage their bookings from their respective websites. Healthcare The mobile app for a medical provider or hospital can allow access to the web-based patient portal (for example, Epic MyChart) without the patient needing to be authenticated in the patient portal again. Streaming and E-Commerce Many consumer-facing streaming and e-commerce applications provide core features through mobile applications such as streaming videos or allowing consumer shopping. They redirect users to the web interface for other features such as account and subscription management. native-to-web SSO will allow consumers to switch to the web interface without re-authentication, thereby improving the user experience. Vendor Portal Similar to corporate portals, organizations typically create mobile applications for B2B portals, such as vendor applications. Vendors can have access to several web-based applications through the portal and hence will benefit from the native-to-web SSO ability. Conclusion Today, applications increasingly use different platforms and devices to provide flexibility to users and allow them to access an application from any place and device. The idea is to bring the applications close to users and allow them to access them from any digital platform that they might be using. Properly authenticating and authorizing users without disrupting their experience and productivity in such an environment is critical. The OIDC bridge complements web-to-web federation and native-to-native SSO standards to provide authentication and SSO services across applications in all possible devices and platforms.

Platform engineering has emerged as a key practice for cloud teams, providing self-service capabilities, automation, and governance to streamline software delivery. This practice has evolved out of scaling out DevOps at a large scale. In this blog, we will explore the role of platform engineering, its benefits, and how Cloud teams can successfully implement it. What Is Platform Engineering? Platform engineering is the practice of designing and building internal developer platforms (IDPs) that enable software teams to develop, deploy, and manage applications efficiently. These platforms integrate tools, infrastructure, and workflows to reduce cognitive load on developers, allowing them to focus on writing code rather than managing complex cloud environments and learning the processes to manage them. Working with development engineering, DevOps, and SRE teams, platform engineers build self-service abstraction layers on top, at times even obfuscating the cloud vendor underneath, so that large development teams get a uniform and simple experience to deploy and manage cloud apps. Platform Engineering for Cloud Teams A well-architected platform engineering approach consists of several key components: Infrastructure and compute – Manages cloud and compute resources needed for workloadsPlatform orchestration layer – Manages automated provisioning and platform-level abstractionsCI/CD and deployment layer – Enables continuous integration, testing, and deploymentObservability and security layer – Ensures visibility, security, and governanceCompliance layer – Baked in Industry compliance control reportingDeveloper experience and self-service layer – Empowers developers with self-service tools and a streamlined workflowCost and FinOps management layer (optimization) – Optimizes cloud usage, tracks cost, and ensures efficiencyAs code assets layer – Infrastructure as Code (IaC), Policy as Code (PaC), Configuration as Code (CaC), and Deployment Architecture as Code (DAaC) Challenges in Setting Up Platform Engineering for Cloud Teams While platform engineering brings significant advantages, cloud teams may face several challenges when implementing it: High initial investment – Building an IDP requires significant time, resources, and expertise, which can be a barrier for some organizations.Specialized skills – Platform Engineers building IDPs need to be experts in various aspects, including development, DevOps, SRE, compliance, and security.Tooling complexity – Selecting and integrating the right set of tools for infrastructure, CI/CD, observability, and security can be overwhelming and requires careful planning.Cultural resistance – Developers and operations teams may resist change, particularly if they are accustomed to traditional workflows. Effective communication and training are essential.Balancing standardization with flexibility – While standardization is crucial, overly rigid platforms may limit innovation. Striking the right balance between governance and developer autonomy is key.Security and compliance challenges – Ensuring security best practices and compliance requirements are consistently met across all teams and environments can be complex.Continuous maintenance and upgrades – Platforms need ongoing maintenance, updates, and improvements, which require dedicated teams and long-term commitment. Benefits of Platform Engineering for Cloud Teams Enhanced developer experience – By abstracting away cloud complexity, developers can focus on coding instead of dealing with infrastructure management.Increased operational efficiency – Automation reduces manual tasks, leading to faster deployments and fewer operational bottlenecks.Improved security and compliance – Predefined security configurations and automated policy enforcement enhance governance and industry compliance across cloud environments.Scalability and reliability – Standardized processes ensure applications are built with scalability and reliability in mind.Cost optimization – Efficient resource allocation and auto-scaling mechanisms help reduce cloud costs. Implementing Platform Engineering in Cloud Teams Here are the high-level steps involved in implementing platform engineering in cloud teams. Step 1: Assess current cloud challenges – Identify pain points such as inconsistent deployments, lack of observability, or security gaps, and multi-cloud environments.Step 2: Define a platform strategy – Outline key objectives, choose appropriate tools, and design an architecture that aligns with business goals.Step 3: Build the internal developer platform – Develop self-service workflows, integrate CI/CD pipelines, and establish governance frameworks.Step 4: Foster a DevOps culture – Encourage collaboration between developers, operations, and security teams to drive platform adoption.Step 5: Continuously iterate and improve – Monitor platform usage, collect feedback, and refine processes to ensure ongoing efficiency. Roles and Responsibilities Each component layer involves specific roles and responsibilities. Here’s how the roles align with each layer: Layer Roles involved Responsibilities Infrastructure and Compute Cloud/Infrastructure Engineer Manages cloud resources, networking, and storage. Platform Engineer Builds and maintains the Kubernetes platform and runtime environments. Site Reliability Engineer (SRE) Ensures availability, scalability, and performance of infrastructure. Cloud Architect Define compute, storage, and networking models at the infrastructure level. Networking Engineer Manages VPCs, subnets, firewalls, load balancers, and DNS. Storage Engineer Oversees block storage, object storage, databases, and backups. Platform Orchestration Platform Engineer Builds internal tooling and automation for developer self-service. SRE Implements Kubernetes operators, service mesh, and autoscaling. Devops Engineer Works on GitOps, infrastructure automation, and configuration management. Manages Configuration as code (CaC) Cloud Architect Architects design how services interact. Maintains Deployment Architectures as code (DAaC) Security Engineer Implements security policies, IAM, and runtime secret protection. CI/CD and Deployment DevOps Engineer Designs and manages CI/CD pipelines. Software Engineer Builds and deploys applications using CI/CD workflows. Security Engineer Implements DevSecOps practices for secure deployments. Observability, Security, and Compliance SRE Monitors system health, performance, and availability. Security Engineer Enforces security policies and compliance. Compliance Engineer Ensures adherence to regulatory frameworks. Maintains Policy as Code (PaC) Developer Experience and Self-Service Platform Engineer Develops self-service portals and golden paths. Developer Advocate Ensures developer satisfaction and productivity. Software Engineer Consumes self-service tools for faster development. Cost and FinOps Management (Optimization) FinOps Engineer Optimizes cloud cost, budgeting, and forecasting. Cloud Architect Designs cost-efficient cloud architectures. Business/Finance Analyst Tracks cloud spending and ensures ROI.As code Assets LayerIaC EngineersManage reusable IaC modules, pipelines, and policies as code. Implements Infrastructure as Code (IaC) with Terraform, Pulumi, or Crossplane.Policy-as-Code ContributorsMaintain versioned, auditable code for infra and securityReusable Asset ManagersEnable modular platform evolution Workflow of IDP The picture below depicts the workflow of the IDP for cloud teams. Conclusion Platform engineering is transforming the way cloud teams build and operate applications at scale by enabling automation, self-service, and standardization. By adopting a platform mindset, organizations can accelerate innovation, enhance security, and improve developer productivity. As cloud complexity continues to grow, investing in platform engineering will be crucial for organizations aiming to stay ahead.

By Josephine Eskaline Joyce CORE

TL; DR: Optimus Alpha Creates Useful Retrospective Format In this experiment, OpenAI’s new stealthy LLM Optimus Alpha demonstrated exceptional performance in team data analysis, quickly identifying key patterns in complex agile metrics and synthesizing insights about technical debt, value creation, and team dynamics. The model provided a tailored retrospective format based on real team data. Its ability to analyze performance metrics and translate them into solid, actionable retrospective designs represents a significant advancement for agile practitioners. The Rationale Behind Analyzing Team Data In this conversation, I explored Optimus Alpha’s capabilities in analyzing team performance data and designing evidence-based retrospectives. The exchange began with a familiar scenario: a Scrum team stuck in a “feature factory” mindset with stakeholders not embracing agile practices. While Optimus Alpha initially provided a solid retrospective format based on this limited information, the real test came when I introduced comprehensive team performance metrics from my AI for Agile Bootcamp cohort class. The synthesized data of MegaBrain.io’s fictitious MB1 team includes metrics on technical debt, value creation, stakeholder satisfaction, and team autonomy across multiple sprints. This simulated dataset represented the complex reality many Scrum teams face — balancing predictability with technical health, stakeholder demands with value creation, and feature delivery with sustainability. What follows is a remarkable demonstration of how Optimus Alpha absorbed this complex information, identified patterns that would typically require an experienced agile practitioner, and then transformed those insights into a tailored retrospective design with supporting materials. The conversation showcases analytical power and the ability to convert data insights into practical tools for team improvement. So, let’s get into the conversation with Optimus Alpha. Analyzing the Team Data With the Help of Optimus Alpha Round 1 I ran this chat on OpenRouter.ai on April 12, 2025. “SW” refers to myself, “OP” to Optimus Alpha. The text is unedited to preserve its authenticity: SW: My Scrum team has had a hard time; we are stuck in feature factory. Our Product Owner is little more than a Jira monkey, and no one is listening to me, the Scrum Master. Can you suggest a retrospective format my team can use to invite our stakeholders and help them understand that we could be more effective if we would only embrace agile practices? OP: Absolutely, and thank you for sharing your challenge so openly — feature factory symptoms and disengaged roles are all too common. It’s great that you’re ready to create space for everyone, including stakeholders, to reflect and learn together. Here’s a retrospective format designed to include stakeholders and shift the conversation toward agile value, not just output: Optimus Alpha — Retrospective Format #1: “From Feature Factory to Value Factory” Aim: Help the whole group — team and stakeholders — reflect on how you work, see the costs of “just pushing features,” and imagine better ways of working together. 1. Setting the Stage (10 min) Welcome everyone, explain the purpose: “We want to reflect together on how we’re working — not just what we’re building — and explore ways to better create value for our customers and business.”Establish Working Agreements (respect, honesty, curiosity).Remind folks: retros are blameless. 2. The Metaphor: “Factory Floor” Visual (5 min) Draw a simple assembly line on a virtual board or whiteboard.On one side, put “Inputs” (ideas, requests); in the middle, “Our Work,” and outputting “Features.”Ask: “Where does customer value show up here?” (Get a few responses from both teams and stakeholders.) 3. Group Activity: “What’s Working, What’s Not?” (15 min) Split everyone into breakout groups (mixed team and stakeholders).Each group answers: What do we do well right now?Where do we see pain, rework, or missed opportunities for value?Use sticky notes (digital or real), and post answers on the board under “Working” and “Not Working.” 4. Share and Cluster Key Themes (10 min) In the main group, everyone shares stickies.Scrum Master (you!) clusters themes — often you’ll see “no time to validate with users,” “little feedback,” “unclear priorities,” etc. 5. Reflection — Are We Doing Agile or Just Doing Stories? (5 min) Present the Agile Manifesto values or Scrum pillars briefly.Invite all: “Where are we supporting these values today? Where are we struggling?”Give a couple of minutes for silent reflection, then discuss. 6. Co-create Experiments (10 min) In pairs (ideally a stakeholder and a team), brainstorm one small experiment for the next sprint that could address a pain point (e.g., getting quick user feedback, clarifying the value of one backlog item, joint story mapping session, etc.).Each pair shares their idea briefly. 7. Closing and Commitment (5 min) Summarize: “If we want to escape feature factory mode, we all have a role.”Ask each participant: “What will I do differently next sprint?”Agree on 1–2 experiments to run, and how you’ll follow up. Tips and Rationale Stakeholders see they’re part of the system — their requests, engagement, and feedback matter.Makes visible the costs of just shipping features (technical debt, missed value, burnout, lack of learning).Creates joint ownership for change, rather than feeling like the Scrum team is just “whining.” Resources You Might Share Feature Factory SignsAgile ManifestoAtlassian’s Retrospective Techniques Let me know if you want some invitation language for your stakeholders or facilitation tips for handling tricky discussions! Note SW: All links are correct topic-wise and technically, although I had to add the title tags manually. Also, this is not just a good retrospective — it’s a transformational opportunity if followed through. It reframes roles, builds bridges, and ignites reflection on what really matters. For a Feature Factory, it’s a worthwhile approach: small risk, huge potential upside. Round 2 SW: Do you need more information from me to improve your retrospective suggestion? OP: That’s a great offer — thank you! Here are some clarifying questions that could help me refine my suggestion, making it more targeted and actionable for your specific context: 1. Stakeholder Context Who are the stakeholders you want to include? (e.g., internal business partners, external clients, managers, VPs…)What do you think “they” understand about Agile or Scrum now?How engaged or disengaged are they in your team’s work or results? 2. Team Dynamics How large is your team? (Size can affect activity facilitation)How long has your team been working in Scrum?How are your team members currently feeling about your process? Are they frustrated, disengaged, or burned out? 3. Pain Points and Symptoms Could you specify a couple of concrete “feature factory” symptoms you’re seeing? (e.g., relentless backlog churning, little feedback from users, no time for reflection/learning, missing sprint goals, etc.)Are there past attempts at improvement or retros that didn’t land well? 4. Goals for the Retrospective What’s your top wish for the outcome? (e.g., awareness/education, spark for change, specific agreement on next steps)Are you hoping to target a specific agile practice or value (focus, transparency, collaboration, delivering value, etc.)? 5. Risk Appetite Would your group be open to a bit of “creative tension,” or do you need to be more diplomatic and gentle in surfacing issues? The more context you can share, the more tailored (and likely effective) I can make the retrospective format, fitting your team’s culture, goals, and challenges. If you’d like, answer whichever of these jumps out, or just share anything else you think is relevant! SW: Before I answer your questions, please analyze the following team performance information and reconsider your questions. (Note: Here, I copied the synthesized team data from the Excel file into the context window.) OP: Thank you for sharing this comprehensive team performance data. It gives a richer, more nuanced picture than any qualitative conversation alone. Before updating questions or tailoring my retrospective advice, here’s a quick analysis of key patterns: Key Patterns and Insights 1. Feature Factory and Agile Health Experiments per sprint: Variable, sometimes 0, often 2–7 (average ~3–4), so the team is experimenting, but not always consistently.Technical debt %: Frequently high (14–25%), showing this is a persistent drain, likely slowing new value delivery.Education time: Fluctuates widely (as low as 1–2 hours, up to 11+), suggesting learning is sometimes deprioritized.Customer interactions: Moderate, but not always present every sprint (often 3–10). Not bad, but more could drive value.Stakeholder satisfaction: Mostly good, though dips into the 4–6 range sometimes with spikes to 9–10 — indicative of variability. 2. Agile Values and Team Dynamics Scores on autonomy, value creation, NPS, professional satisfaction, psychological safety, courage, respect, openness, and focus mostly trend between 5 and 9, but all dip into the 3–5 range at times, signaling periods of stress (see sprints 5–6, 15, etc.).Diary entries frequently mention: Management interferenceTechnical debt slowing progressPositive retros/workshops that seem to spike morale/value scoresStakeholder tensions, executive pressure, and production outages 3. Other Patterns Predictability is high (60–90%), but that doesn’t always equate to satisfaction or value.Escaped defects, incidents, and new/lost team members are low: this is a stable team, but with recurring process pain. What This Means for Your Retrospective The Team is NOT “Just a Feature Factory” There is some experimentation, customer contact, and educational investment.However, periods of “feature factory stress” (management interference, tech debt response mode, executive pressure) very clearly drive low scores in autonomy, satisfaction, and value creation. When the Team Works Well High autonomy and psychological safety, productive retros, stakeholder workshops, customer feedback => spike in both business and team satisfaction. Biggest Opportunities Consistent focus on value over pure output — variability is still high.Creating a more stable environment for learning, retrospection, and customer feedback, not just monthly or in “good times.”Better defense against management micromanagement and shifting priorities.Bridging perception with stakeholders — communicating value, not just throughput. Revised Follow-Up Questions With this data, fewer clarifying questions are needed, but I’ll tune them: What are your goals for the next retrospective, given these patterns? Awareness/education for stakeholders?Specific improvements (tech debt, stakeholder engagement, value focus)?Who do you want stakeholders to hear from? The team as a whole?Specific customer voices or data?Are there “untouchable” topics, or are you willing to openly discuss management interference and tech debt as a group?What timeframe do stakeholders care about most (week-to-week, quarterly outcomes, etc.)? How much appetite do they have for participating?In your view, which ONE success from the data is worth celebrating with stakeholders, and which ONE pain would you like to see acknowledged or solved? If you answer even a couple of these, I can create a high-impact, data-driven retrospective format (perhaps even using this data as a visual centerpiece to spark joint learning and action among stakeholders and the team). Would you like a sample format that incorporates this data as a mirror for your group? Or do you want to further discuss the questions above? SW: Regarding your follow-up questions: Help the stakeholders understand that the team could create more value for customers and the organization if we leave the feature factory and embrace agile product development. It is about education, opportunities, trust building, overcoming technical debt.The team as a whole.No.At the moment, they are focused on the team meeting its commitments as defined in their year-long product roadmap, a problematic approach in a complex environment.Celebrate: High predictability despite the circumstances. Pain point: technical debt accumulation. Please revise your suggested retrospective format. OP: Thank you for the clear answers! Here’s an improved retrospective format tailored to your needs, data, and objective to educate and inspire stakeholders, shifting them from feature factory thinking to agile, value-driven product development. Retrospective Format #2: “Beyond Commitments: Building Lasting Value” Audience: Scrum team and stakeholdersDuration: 60–75 minutesGoal: To build trust and educate stakeholders on how greater agility, autonomy, and focus on technical health can boost business value — beyond simply hitting features on a roadmap. 1. Welcome and Framing (5 min) “We’ve been very predictable, but our team sees even more potential for value — if we work together to modernize how we deliver.”Ground rules: curiosity, no blame, focus on opportunities.State purpose: “Let’s look at the real story our data tells, and find ways to unlock even more value for our customers and business.” 2. Celebrating Predictability (5 min) Show a simple chart of your team’s high predictability.Acknowledge: “We’re proud to honor commitments, despite challenges. That’s a real team achievement worth celebrating!” 3. Looking Deeper: The Value vs. Output Exercise (10 min) Show two simple graphs: a) Predictability (high, steady)b) Technical debt/Value creation/Stakeholder satisfaction (variable/fluctuating)Prompt: “What do you notice? What stories hide behind the numbers?”Invite both team and stakeholder reactions. 4. Short Team Stories: What’s the Cost of Feature Factory Living? (10 min) Team members each briefly share: A time when technical debt or backlog pressure forced a tradeoff.A time when autonomy/conversation with customers led to real value or insight.Ask: “What might have been possible, if we’d invested differently?” 5. Joint Brainstorm: Obstacles to Value (15 min) Use two columns (“What helps us create value?” / “What blocks it?”) The team and stakeholders add stickies or notes.Common “blockers”: technical debt, rigid roadmaps, lack of room to learn or adapt, excessive management intervention.Common “helpers”: customer feedback, refactoring, team autonomy, experiments, stakeholder trust.Cluster and discuss. Highlight technical debt as the recurring pain point. 6. Opportunities for Change: Data-Driven Reflection (15 min) Show a visual of technical debt trend and its correlation with value creation or satisfaction (even rough/qualitative is fine: “Our biggest dips in value/satisfaction align with tech debt crunch periods…”)Prompt stakeholders: “If we could reduce tech debt and invest in technical health, what new doors might open?”“What would make you more confident to allow the team to explore, experiment, or adjust course?”“How might we move from only delivery, to creating value that lasts?” 7. Pick 1–2 Team + Stakeholder Experiments (10 min) Examples (modify as needed): Block 10% of time for tech debt/health work every sprint; review outcomes with stakeholders.Monthly customer feedback or “value alignment” review — invite a stakeholder to join.Jointly define what “real value” means for a sprint or feature.Hold quarterly review: “Is our roadmap still the highest-leverage way to deliver value?”Agree to try one or two for the next sprint/month. 8. Commitment Rounds and Closing (5 min) Each person (including stakeholders): “One thing I commit to do, say, or advocate for in service of value creation, not just feature delivery.”Thank everyone for honesty, curiosity, and partnership. Facilitation Pointers Use rich data visuals — a couple easy graphs (predictability, tech debt, value creation, satisfaction) spark honest discussion.Encourage the team to speak frankly about technical debt consequences.Invite stakeholders to reflect on what *they* would gain if the team could work with more agility and autonomy (“would we get to market faster, respond to changes better, attract and retain top talent?”).Model mutual respect: “Everyone here wants to deliver business value — but the path isn’t always just through features.” Optional Pre-Work Consider sending the predictability and tech debt charts (and maybe a value creation graph) to stakeholders ahead of the session, with a note: “We’re incredibly proud of our team’s reliability, yet we see a chance to unlock even more business value by working smarter. Looking forward to exploring this with you in our next session!” Let me know if you want: Example data slide templatesStickies/board templates for Miro/Mural/JamboardMore story prompts for your team This format will help you shift the conversation from mere throughput (“predictable factory”) to real, meaningful, sustainable value — for both customers and the business. Note SW: The revised retrospective format is an interesting outcome: Adding real team data helped shift our retrospective from general discussion to focused conversations about what really affects results. Instead of just talking about feelings or processes, we looked at facts — like predictability, tech debt, and team satisfaction. This shift would make it easier for everyone, including stakeholders, to see where we are stuck and what is possible. It turned vague concerns, addressed by the first format, into clear opportunities for smarter decisions and better long-term outcomes. This development perfectly reflects one of the outstanding capabilities of large language models: analyzing large quantities of data, identifying patterns within them, and suggesting next steps. And Optimus Alpha proves to be really good at this. Of course, I asked to deliver all of the before-mentioned artifacts only to be disappointed; Optimus Alpha could not deliver yet. Conclusion: Optimus Alpha’s Retrospective Formats Optimus Alpha’s ability to analyze team data and design targeted retrospectives demonstrates the transformative potential of AI for agile practitioners. Tools like Optimus Alpha can help teams escape the feature factory trap and build more sustainable, value-focused ways of working by quickly identifying patterns in complex metrics and translating them into actionable formats. The model’s insight into the relationship between technical debt and value creation, team autonomy, and stakeholder satisfaction provides evidence-based foundations for retrospectives that might otherwise rely solely on subjective experiences. This approach represents a new frontier for data-informed facilitation — where LLMs serve as information processors and insightful partners in team improvement, easing communication with more data-oriented stakeholders. As agile practices continue to evolve, the opportunity to augment your retrospectives with AI-assisted data analysis could be game-changing: no more “gut-feeling-driven” change requests! Have you experimented with analyzing team data using AI tools? How might an LLM like Optimus Alpha help you identify patterns in your team’s metrics that could inform more impactful retrospectives? Please share your experiences and thoughts on how this approach might enhance your team’s journey toward greater business agility and sustainable value delivery.

By Stefan Wolpers CORE

Kubernetes Site Reliability Engineers (SREs) frequently encounter complex scenarios demanding swift and effective troubleshooting to maintain the stability and reliability of clusters. Traditional debugging methods, including manual inspection of logs, event streams, configurations, and system metrics, can be painstakingly slow and prone to human error, particularly under pressure. This manual approach often leads to extended downtimes, delayed issue resolution, and increased operational overhead, significantly impacting both the user experience and organizational productivity. With the emergence of AI-powered solutions, innovative tools like k8sgpt and DeepSeek are revolutionizing how Kubernetes SREs approach troubleshooting. Using advanced AI reasoning capabilities, these intelligent assistants provide real-time, actionable insights and guided recommendations directly within Kubernetes environments. Such technology drastically reduces mean time to resolution (MTTR) by quickly pinpointing root causes, recommending precise corrective actions, and streamlining overall operational efficiency. In essence, adopting AI-driven troubleshooting copilots empowers Kubernetes SREs to maintain robust, resilient clusters with unprecedented ease and effectiveness. GROQ: Gateway to Deepseek What Is Groq? Groq refers to Groq Cloud, a platform providing fast inference APIs for powerful LLMs, similar to OpenAI or Anthropic. Groq offers access to state-of-the-art models such as Meta's Llama-3 series and other open-source foundation models, optimized for high-speed inference, often at lower latency and cost compared to traditional cloud AI providers. Key Highlights LLM inference APIs. Access models like Llama-3-70B, Llama-3-8B, Mixtral, Gemma, and others.Competitive advantage. Extremely fast model inference speeds, competitive pricing, and simpler integration.Target users. Developers, enterprises, and startups need quick, scalable, and cost-effective AI inference. Groq follows the OpenAI API format, which allows us to use the DeepSeek LLM inside k8sgpt under the backend named openai while leveraging Groq’s high-performance inference capabilities. In this article, we will explore how k8sgpt, integrated with DeepSeek using Groq API, can help troubleshoot a Kubernetes cluster in real time. By the end of this guide, you’ll have a fully operational AI-powered Kubernetes troubleshooting RAG AI agent (Kubernetes SRE Copilot) at your disposal. Steps to Power Kubernetes Cluster by AI (Deepseek) 1. Setting up a Kubernetes Cluster Using KIND Before we start troubleshooting, let’s set up a local Kubernetes cluster using KIND (Kubernetes IN Docker). Step 1: Install KIND Ensure you have Docker installed, then install KIND: Shell curl -Lo ./kind https://kind.sigs.k8s.io/dl/v0.26.0/kind-linux-amd64 chmod +x ./kind mv ./kind /usr/local/bin/kind Step 2: Create a Cluster Shell kind create cluster --name k8s-demo Verify the cluster setup: Shell kubectl cluster-info --context kind-k8s-demo Now that we have our cluster running, we can move on to setting up k8sgpt. 2. Installing and Configuring k8sgpt Step 1: Install k8sgpt Shell curl -s https://raw.githubusercontent.com/k8sgpt-ai/k8sgpt/main/install.sh | bash Verify installation: Shell k8sgpt version Step 2: Configure k8sgpt to Connect to the Cluster Shell kubectl config use-context kind-k8s-demo k8sgpt version At this point, k8sgpt is installed and ready to analyze Kubernetes issues. However, we need an AI backend to process and explain the errors. Let’s set up DeepSeek using Groq API for this. 3. Obtaining Groq API Keys To use DeepSeek via Groq, we need an API key from Groq. Go to Groq API.Sign in or create an account.Navigate to the API section and generate an API key.Copy the API key securely. Once we have the API key, we can configure k8sgpt to use it. 4. Setting Up k8sgpt Authentication With Groq We will configure k8sgpt to use OpenAI’s backend, but point it to Groq API as the base URL and model as DeepSeek. Shell k8sgpt auth update -b openai --baseurl https://api.groq.com/openai/v1 --model deepseek-r1-distill-llama-70b -p <YOUR_GROQ_API_KEY> Verify authentication: Shell k8sgpt auth list If the credentials are correct, you should see openai as an available backend. 5. Deploying a Sample Application in the Weather Namespace Let’s deploy a sample weather application in a weather namespace to test troubleshooting. Shell kubectl create namespace weather kubectl apply -f https://raw.githubusercontent.com/brainupgrade-in/obs-graf/refs/heads/main/prometheus/apps/weather/weather.yaml -n weather Check if the pods are running: Shell kubectl get pods -n weather If there are errors, we can analyze them using k8sgpt. 6. Using k8sgpt in Interactive Mode for Live Troubleshooting We can now use k8sgpt to analyze and fix issues interactively. Let us scale down the weather replicas to 0 (kubectl scale --replicas 0 deploy weather -n weather) and see if k8sgpt can detect the issue and help troubleshoot. Shell k8sgpt analyze -n weather --explain -i This command will scan logs, events, and configurations to identify potential issues and provide AI-assisted troubleshooting steps. See below the video demonstrating how this k8sgpt as RAG AI Agent acting as SRE Copilot helps do live troubleshooting! Kubernetes SRE Copilot using k8sgpt and DeepSeek Conclusion With k8sgpt and DeepSeek via Groq, Kubernetes SREs now have a powerful AI-driven copilot that dramatically simplifies and accelerates troubleshooting. This innovative solution automates the complex and tedious processes of issue identification and root cause analysis, delivering precise insights rapidly. Furthermore, the interactive CLI offers step-by-step guidance, enabling engineers to apply accurate fixes confidently and efficiently, significantly reducing the time typically spent on manual diagnostics and repairs. The integration of AI with Kubernetes operations is undeniably transforming the future of site reliability engineering. Tools like k8sgpt and DeepSeek streamline cluster management and substantially enhance reliability, resilience, and overall operational effectiveness. Embracing this technology empowers Kubernetes SREs to proactively address issues, maintain continuous availability, and easily optimize infrastructure. Experience the remarkable efficiency of AI-driven troubleshooting by integrating k8sgpt into your Kubernetes workflows today!

By Rajesh Gheware CORE

Site reliability engineering (SRE) plays a vital role in ensuring Java applications' high availability, performance, and scalability. This discipline merges software engineering and operations, aiming to create a robust infrastructure that supports seamless user experiences. In this article, we will delve into a range of best practices aligned with SRE principles, including monitoring, logging, alerting, performance optimization, disaster recovery, security, automation, and incident management. Each section will be illustrated with relevant Java code samples to provide practical insights. The core objective of SRE is to strike a balance between rapid feature development and the stability of systems. By doing so, SRE nurtures the long-term reliability of applications, even as they scale to accommodate increasing user demands. This approach relies on several key strategies: Proactive monitoring. Implementing comprehensive monitoring solutions allows teams to track application performance and user experience metrics continually. Tools like Prometheus or Grafana can help visualize and analyze these metrics in real time.Automated failure detection. Setting up mechanisms for the automated detection of failures ensures that issues are identified swiftly. This can involve using alerting systems that notify teams of abnormalities before they escalate into significant outages.Efficient incident response. A well-defined incident management process is crucial. This includes documenting procedures, conducting post-mortems after incidents, and implementing changes to prevent recurrence.Performance optimization. Regularly profiling applications and optimizing resource usage can lead to significant performance improvements. Techniques such as caching, efficient database queries, and code optimization are essential for maintaining responsiveness.Disaster recovery planning. Preparing for unexpected incidents through effective disaster recovery strategies ensures that systems can be restored quickly. This involves creating backups, failover systems, and testing recovery plans regularly.Security measures. Integrating security into the development lifecycle is imperative. This includes regular vulnerability assessments and adopting secure coding practices to protect applications from threats.Automation. Automating repetitive tasks not only improves efficiency but also reduces the likelihood of human error. This can cover everything from deployment processes to scaling infrastructure based on demand. With these principles, organizations can achieve higher reliability and streamline their operations, ultimately leading to a better experience for end users. Through the thoughtful implementation of SRE practices, development teams can ensure that their Java applications are resilient and performant, ready to meet the challenges of a rapidly evolving technological landscape. 1. Monitoring and Observability Observability plays a crucial role in understanding the behavior of applications and identifying failures at an early stage. To achieve effective observability, monitoring solutions should comprehensively capture a range of data, including metrics, logs, and traces. Metrics provide quantitative insights into system performance, while logs record events and states, offering context for those metrics. Traces, on the other hand, help track the flow of requests through various services, revealing interdependencies and potential bottlenecks. Without a robust monitoring strategy in place, diagnosing performance issues and system failures can become extremely challenging. This lack of visibility can lead to prolonged downtime, frustrated users, and increased operational costs. By implementing effective observability practices, organizations can detect and address issues more swiftly and gain a deeper understanding of their application’s performance over time, leading to improved reliability and user experience. Using Micrometer and Prometheus for Metrics Micrometer is an advanced library designed for collecting and managing application metrics in Java environments. It offers a flexible abstraction over a variety of monitoring backends, including popular options like Prometheus, Datadog, and New Relic. This integration simplifies the process of tracking system health and performance by allowing developers to seamlessly switch between different monitoring solutions without significant code changes. With Micrometer, developers can gather detailed metrics on application behavior, such as response times, error rates, and resource usage, providing valuable insights that help improve reliability and optimize system performance. Here's how you can integrate it with Prometheus: Java import io.micrometer.core.instrument.MeterRegistry; import io.micrometer.core.instrument.Timer; import org.springframework.web.bind.annotation.GetMapping; import org.springframework.web.bind.annotation.RequestMapping; import org.springframework.web.bind.annotation.RestController; @RestController @RequestMapping("/metrics") public class MetricsController { private final Timer responseTimer; public MetricsController(MeterRegistry registry) { this.responseTimer = registry.timer("http_request_duration_seconds"); } @GetMapping("/request") public String trackRequest() { return responseTimer.record(() -> { try { Thread.sleep((long) (Math.random() * 1000)); } catch (InterruptedException e) { Thread.currentThread().interrupt(); } return "Request Processed"; }); } } This code is designed to monitor the duration of HTTP requests, capturing important timing data that is formatted to be compatible with Prometheus, a popular open-source monitoring and alerting toolkit. Exporting these metrics to Prometheus allows for efficient collection and visualization of the data. This setup empowers engineers to easily spot latency spikes and identify potential bottlenecks in their systems, facilitating quicker troubleshooting and performance optimization. Distributed Tracing With OpenTelemetry OpenTelemetry is a powerful framework that provides distributed tracing, a crucial tool for diagnosing performance bottlenecks in complex systems. By tracking the path of requests as they travel across multiple services, OpenTelemetry enables developers and operators to gain insights into the behavior and performance of their applications. This capability is particularly valuable in microservices architectures, where a single request may interact with numerous independent components. Understanding how these components work together and identifying where delays or issues occur will help teams effectively optimize their systems, enhance user experience, and improve overall application reliability. Additionally, the rich context provided by distributed tracing allows for more informed decision-making when it comes to scaling and maintaining these services. Java import io.opentelemetry.api.trace.Span; import io.opentelemetry.api.trace.Tracer; public class TracingExample { private final Tracer tracer; public TracingExample(Tracer tracer) { this.tracer = tracer; } public void processRequest() { Span span = tracer.spanBuilder("processRequest").startSpan(); try { // Simulate work Thread.sleep(500); } catch (InterruptedException e) { Thread.currentThread().interrupt(); } finally { span.end(); } } } OpenTelemetry allows developers to visualize the flow of requests, identify slow services, and optimize performance effectively. 2. Logging and Alerting Robust logging and alerting systems are vital for engineers to swiftly identify and resolve issues in real time. Comprehensive logging ensures that critical data is accurately recorded, allowing for thorough analysis and understanding of events leading up to a problem. This information can include error messages, timestamps, and contextual details that paint a clear picture of system behavior. In addition to that, effective alerting mechanisms play a crucial role in proactive issue management. These systems automatically notify relevant teams about potential failures or anomalies, ensuring that issues are addressed before they escalate into more significant problems. By combining detailed logging with efficient alerting, organizations can enhance their operational reliability and responsiveness, ultimately leading to improved system performance and user satisfaction. Implementing Structured Logging With Logback Structured logging enhances log readability and simplifies analysis. Using Logback, we can set up structured logging in a Spring Boot application: XML <configuration> <appender name="FILE" class="ch.qos.logback.core.rolling.RollingFileAppender"> <file>logs/app.log</file> <encoder> <pattern>%d{yyyy-MM-dd HH:mm:ss} [%thread] %-5level %logger{36} - %msg%n</pattern> </encoder> </appender> <root level="info"> <appender-ref ref="FILE" /> </root> </configuration> Setting Up Alerts With Prometheus and Alertmanager Prometheus Alertmanager is a powerful tool designed to help users set up alerts based on specific metric thresholds. Monitoring key performance indicators ensures that system administrators are promptly notified of any issues that may arise. For instance, to create a straightforward rule that triggers alerts when latency exceeds an acceptable level, you can configure it as follows: JSON - alert: HighLatency expr: http_request_duration_seconds{quantile="0.95"} > 1 for: 5m labels: severity: critical annotations: summary: "High request latency detected" 3. Performance Optimization Optimizing application performance is crucial for maintaining responsiveness and scalability, especially in environments with varying loads. Through techniques like caching, developers can store frequently accessed data in memory, reducing the need to retrieve it from slower databases and thus improving response times. Connection pooling allows applications to reuse existing database connections, minimizing the overhead associated with establishing new connections for each request. Additionally, asynchronous processing enables tasks to run concurrently, allowing the application to handle multiple operations simultaneously without blocking the main execution thread. Together, these techniques significantly enhance the efficiency and performance of Java applications, ensuring they can handle increasing user demands effectively. Using Caching With Spring Boot and Redis Using Redis caching is an effective strategy to alleviate database load and significantly improve the speed of request processing in applications. By temporarily storing frequently accessed data in memory, Redis allows for quicker data retrieval compared to fetching it from a traditional database. To illustrate how to integrate Redis caching into a Java application, we can consider a simple example. This process typically involves setting up a Redis server, adding the necessary dependencies to your Java project, and implementing caching logic in your application code. Here’s a step-by-step approach to get you started: Step 1: Set Up Redis Make sure you have a Redis server running, either locally or hosted through a cloud provider. Step 2: Add Dependencies Include the Redis client library in your Java project. If you're using Maven, you can add the following dependency to your pom.xml: XML <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-data-redis</artifactId> </dependency> Step 3: Configure Redis In your application properties file, configure the connection settings for Redis. JSON spring.redis.host=localhost spring.redis.port=6379 Step 4: Implement Caching Logic Use Redis annotations or directly access the Redis template to place and retrieve cached data. For example, you might use @Cacheable to indicate that the result of a method should be cached: Java @Cacheable("items") public Item getItemById(String itemId) { return itemRepository.findById(itemId).orElse(null); } Java import org.springframework.cache.annotation.Cacheable; import org.springframework.stereotype.Service; @Service public class DataService { @Cacheable("data") public String getData(String key) { return "Cached data for key: " + key; } } With these steps, you can integrate Redis caching into your Java application, resulting in reduced database load and improved performance for users. Keep in mind that effective caching also requires strategies for cache invalidation and data consistency, which are important for maintaining the accuracy of your application’s data. Optimizing Database Connections With HikariCP HikariCP is a high-performance JDBC connection pool that minimizes latency and enhances database efficiency. Configure it in application.properties: JSON spring.datasource.hikari.maximum-pool-size=10 spring.datasource.hikari.minimum-idle=2 spring.datasource.hikari.connection-timeout=30000 Using HikariCP guarantees efficient database connectivity while minimizing resource consumption. 4. Disaster Recovery and Fault Tolerance Disaster recovery strategies play a crucial role in maintaining business continuity by systematically minimizing downtime and data loss during unforeseen events. These strategies encompass a range of practices that help organizations prepare for and respond to disasters, ensuring that critical operations can resume quickly. In addition to these strategies, implementing fault tolerance mechanisms is vital for enhancing system reliability. These mechanisms are designed to allow applications to continue functioning with minimal disruption, even when failures occur. By incorporating redundancy, error correction, and graceful degradation, businesses can ensure that their systems recover smoothly from failures, thereby protecting data integrity and maintaining uninterrupted service for their users. Together, these approaches not only safeguard vital information but also bolster customer trust and confidence in the organization’s resilience. Implementing Circuit Breaker With Resilience4j Java import io.github.resilience4j.circuitbreaker.annotation.CircuitBreaker; import org.springframework.stereotype.Service; @Service public class PaymentService { @CircuitBreaker(name = "paymentService", fallbackMethod = "fallback") public String processPayment() { throw new RuntimeException("Payment service unavailable"); } public String fallback(Exception e) { return "Fallback: Payment service is currently down."; } } 5. Incident Response and Postmortems Incident response plays a vital role in swiftly addressing and mitigating production issues, ensuring minimal impact on users and business operations. Additionally, conducting thorough postmortem analyses allows teams to reflect on failures, fostering a culture of continuous improvement and preventing similar incidents in the future. Steps for Effective Incident Management The following steps will help teams ensure a comprehensive approach to incident management, improving their readiness and response to any future challenges. Detection Establish robust monitoring systems that use alerts and dashboards to identify and flag anomalies in real time. This can involve setting thresholds for performance metrics so that deviations are promptly noticed. Diagnosis Conduct a detailed investigation of the system logs, performance metrics, and other relevant data to accurately pinpoint the root causes of the incident. Collaborating with team members across various domains can enhance the diagnostic process. Resolution Implement solutions swiftly, which may include deploying code fixes, updating configurations, or rolling back to a previous stable state. It is crucial to communicate the resolution steps clearly to all stakeholders involved, ensuring everyone is informed. Postmortem Analysis After resolving the incident, convene a meeting with all relevant team members to document what was learned from the event. This analysis should include discussing what went wrong, what went well, how the incident was handled, and actionable items for process improvements moving forward. A well-documented postmortem can serve as a valuable resource for future reference and training. 6. Security Best Practices Security is a crucial element of site reliability engineering (SRE), as it plays a significant role in maintaining the integrity and availability of applications. A robust security framework not only protects sensitive user data but also reduces the risk of breaches, unauthorized access, and data leaks. By implementing security best practices such as regular vulnerability assessments, access controls, and data encryption, organizations can safeguard their applications against increasingly sophisticated cyber threats. Additionally, incorporating security measures throughout the software development lifecycle ensures that vulnerabilities are identified and addressed early, promoting a secure environment for both users and the organization. For Java-based systems, integrating frameworks like Spring Security is essential. Like other Spring Boot components, Spring Security is user-friendly and easy to learn, making it simple to implement security measures within applications: Java @Configuration @EnableWebSecurity public class SecurityConfig extends WebSecurityConfigurerAdapter { @Override protected void configure(HttpSecurity http) throws Exception { http .authorizeRequests() .anyRequest().authenticated() .and() .httpBasic(); } } 7. Automated Testing and CI/CD Pipelines Automating the testing and deployment processes significantly reduces the likelihood of human errors and enhances overall reliability. In our development workflow, we typically utilize JUnit, a popular testing framework for Java applications, to perform unit testing and ensure that individual components function as intended. Additionally, we sometimes implement mocking tools, such as Mockito or EasyMock, to simulate complex behaviors and dependencies within our code. This allows us to isolate specific parts of the application during testing, making it easier to identify and address bugs efficiently, while also reducing the overhead of integrating with external systems. Overall, automation in testing and deployment leads to more consistent application performance and a smoother release process: Java import static org.junit.jupiter.api.Assertions.*; import org.junit.jupiter.api.Test; class CalculatorTest { @Test void testAddition() { assertEquals(5, Calculator.add(2, 3)); } } 8. Chaos Engineering for Reliability This systematic discipline empowers organizations to create resilient and fault-tolerant systems by intentionally injecting failures into their infrastructure, applications, or networks. This proactive approach aims to uncover vulnerabilities and weaknesses in systems before they lead to catastrophic real-world outages, thereby ensuring higher availability and performance. Key Concepts of Chaos Engineering for Reliability Proactive Failure Testing Instead of adopting a reactive stance where issues are addressed post-failure, chaos engineering equips teams with the tools to simulate real-world incidents in a controlled setting. This allows organizations to proactively identify and address potential failures before they escalate. Hypothesis-Driven Experiments Before introducing failure conditions, engineers develop a detailed hypothesis regarding the expected system behavior, including response times and error rates. This focused approach enables teams to gauge whether the system behaves as predicted. If the system fails to meet the expected outcomes, it signals underlying weaknesses that can be systematically addressed. Gradual and Controlled Experiments Chaos experiments commence on a small scale, targeting specific components or services within the system. As confidence builds, the complexity and scope of the experiments can be gradually increased. This method minimizes the risk of unintended consequences that could adversely affect overall system performance. Observability and Monitoring Effective chaos engineering relies on comprehensive observability strategies. Metrics such as latency (response time under stress), throughput (amount of processed data), error rates (frequency of errors encountered), and availability (system uptime) are rigorously monitored and analyzed to assess system behavior under failure conditions. Advanced logging and tracing techniques further enhance visibility into system performance. Automated and Continuous Testing Many organizations are integrating chaos engineering practices into their continuous integration and continuous deployment (CI/CD) pipelines. This ensures that reliability issues are identified and addressed early in the software development lifecycle, allowing teams to maintain higher quality standards and reduce the likelihood of production failures. Common Chaos Engineering Techniques Latency Injection Simulating delayed network responses to assess how systems adapt to slowdowns, which can occur in real-world scenarios such as network congestion or server overload. Packet Loss and Network Partitioning Testing how applications and services handle unexpected connectivity issues, such as dropped packets or complete loss of network segments, to identify weaknesses in failover mechanisms. Process and Server Kill Experiments Intentionally stopping critical services or shutting down server instances to evaluate how well the system can recover and maintain service continuity through redundancy and failover strategies. CPU and Memory Stress Tests Overloading system resources to determine if auto-scaling configurations are effective and how the system behaves under extreme usage conditions, which simulate spikes in demand. Popular Chaos Engineering Tools Netflix Chaos Monkey A highly effective tool that randomly terminates cloud instances within a production environment to test the resilience of applications and ensure they can withstand service disruptions. Gremlin An enterprise-grade platform designed for chaos engineering that provides a user-friendly interface for running controlled experiments, allowing teams to simulate various failure scenarios without risking production stability. Chaos Mesh A Kubernetes-native chaos engineering framework that enables users to define, manage, and execute chaos experiments in containerized environments, ensuring consistent reliability testing within cloud-native applications. LitmusChaos An open-source chaos engineering solution tailored for Kubernetes workloads that provides a suite of tools and pre-defined experiments to identify vulnerabilities and improve application resilience. Why Is Chaos Engineering Important for Reliability? Prepares teams for real-world failures. By actively engaging in failure simulations, teams are better equipped to respond efficiently to actual incidents, leading to reduced recovery times.Reduces downtime and enhances service level agreements (SLAs). Effective testing minimizes the risk of outages, contributing to better customer experiences and meeting contractual obligations for service availability.Improves system resilience by uncovering weak points. Through rigorous testing, organizations identify areas for improvement, leading to more robust architectures that can withstand unexpected challenges.Fosters a culture centered around reliability and observability. Chaos engineering promotes an organizational mindset focused on continuous improvement, encouraging teams to take ownership of system reliability and prioritize proactive monitoring and testing initiatives. Conclusion Implementing SRE best practices enables Java applications to achieve enhanced reliability, resilience, and scalability. Proactive monitoring and observability empower teams to identify and address issues promptly. Logging and alerting facilitate swift detection and resolution of system failures, while performance optimization ensures efficient scaling to meet growing demands. Disaster recovery strategies and fault tolerance mechanisms minimize downtime, safeguarding application availability. Security best practices protect against cyber threats, and automation ensures consistency in deployments and testing processes. By adopting these practices, development and operations teams can build robust, scalable, and secure Java applications that deliver a seamless user experience.

By Reza Ganji CORE

Agile methodologies were introduced to enhance collaboration, foster continuous improvement, and promote the growth of both software and people. The intent was to create a framework that not only improved delivery but also empowered teams to innovate, learn, and contribute beyond their immediate tasks. However, what started as a noble idea has, in many cases, devolved into a system focused purely on delivery. Teams are increasingly confined to ticking checkboxes in user stories, following rigid processes, and participating in endless meetings, leaving little room for creativity, autonomy, or professional growth. The Current Reality of Agile: A Shift from Vision to Execution Agile today is often project-driven rather than people-driven, especially in the context of frameworks like Scrum. The shift is evident in several ways: Checklist mentality. Engineers, especially QA professionals, find themselves restricted to executing predefined tasks from Product Owners (POs) or Project Managers (PMs). User stories dictate the work, leaving little space for engineers to question, innovate, or explore.Overload of meetings. Scrum ceremonies, combined with interactions across cross-functional teams, consume a significant amount of time, reducing bandwidth for deep work or skill development.Limited ownership. Requirements often come from architects and managers, while engineers become executors rather than contributors to design or strategy. Impact on QA Professionals QA professionals are particularly affected by this shift, despite Agile’s original promise of a collaborative, quality-focused environment. Confined roles. QA is often expected to simply validate acceptance criteria in user stories, limiting exploratory testing, risk assessment, and test strategy improvements.Focus on speed over quality. The pressure to deliver within short sprints pushes QA to prioritize speed over depth, potentially compromising product quality.Stagnation of Skills: With little time for learning or innovation, QA professionals find fewer opportunities to grow, upskill, or adopt new tools and practices. QA can still play a larger role in Agile by contributing to preventative testing, automation strategies, and cross-team collaboration. Their involvement in risk management and continuous feedback loops can enhance product quality if given the autonomy to explore these areas. Key Reasons Agile Becomes Delivery-Focused Several factors contribute to this shift: Misinterpretation of Agile. Many organizations implement Agile as a checklist-driven process rather than embracing its core principles of adaptability and collaboration.Stakeholder pressure. The emphasis on quick releases and hitting deadlines often overshadows the need for quality and team well-being.Rigid frameworks. Frameworks like Scrum can become bureaucratic if followed dogmatically, leaving little flexibility for innovation or team-driven improvements. These challenges are not inherent to Agile or frameworks like Scrum but arise from rigid, checklist-driven implementations often driven by external pressures. Rebalancing Agile: Strategies for Change To restore Agile’s original intent and create an environment where both delivery and people thrive, organizations can adopt the following strategies: 1. Evolve to Flexible Models Move from rigid Scrum frameworks to Kanban or hybrid models that emphasize flow and continuous delivery over fixed sprint cycles. This flexibility gives teams the space to focus on quality, learning, and continuous improvement. 2. Revisit Core Agile Values Agile should prioritize: Individuals and interactions over processes and tools.Working software over comprehensive documentation. Leadership should advocate for these principles, fostering collaboration, creativity, and ownership over rigid adherence to processes. 3. Promote Autonomy Empower teams to own their work and make decisions on how to achieve outcomes. QA professionals should have the freedom to innovate, explore risks, and suggest improvements. Measure the Right Metrics Move away from traditional metrics like velocity and burndown charts. Focus instead on: Customer satisfactionQuality improvementsTeam engagement and morale Create Space for Growth Allocate time within sprints for learning, skill development, and innovation. Encourage teams to experiment and take ownership of their growth. Streamline Meetings Evaluate the necessity of every meeting. Fewer, more focused meetings can free up time for deep work and self-improvement. Continuous Improvement Beyond Retrospectives Introduce improvement sprints focused on technical debt, automation improvements, or skill development. These sprints can enhance team capabilities and product quality. Leadership Accountability Agile transformation should be both top-down and bottom-up. Leaders need to foster an environment that values people’s growth, continuously advocating for a culture of learning and improvement. Adopt a Product Mindset Shift from feature-driven development to focusing on long-term value delivery and continuous product improvement. This mindset emphasizes sustainable growth over short-term feature completion. Conclusion Agile was never meant to be about just delivery. It was designed to empower teams to deliver value, grow continuously, and improve both the product and themselves. By revisiting Agile’s core principles and fostering a culture of learning, collaboration, and autonomy, organizations can realign with its original intent. By shifting the focus back to people, Agile can once again become a framework that inspires, empowers, and enables true growth — both for products and the professionals building them.

Objective A common challenge for engineering leadership is the management of non-project backlogs, which consist of unresolved issues from previous projects, such as technical debt, outstanding bugs, and incomplete documentation. This article examines the inherent difficulties and proposes implementation guidelines for mitigating the negative impact of these backlogs on current development efforts, ultimately aiming to enhance productivity and project success. Overview Non-project backlogs consist of tasks that, while not directly tied to current project deliverables, still require attention and consume engineering time. These tasks often originate from previous project work, maintenance, or support activities and, if left unmanaged, can negatively impact current projects and the morale of software engineers. The following sections detail common examples of non-project backlog items and strategies for effective management. Technical Debt This includes code that was implemented quickly or with suboptimal solutions to meet deadlines in past projects. This shortcutting creates "debt" that needs to be "repaid" later through refactoring, code cleanup, or more robust implementations. Examples include: Inconsistent coding styles. Variations in formatting, naming conventions, and other stylistic elements that make the codebase less cohesive.Missing unit tests. Lack of automated tests that verify the correctness of the code.Known bugs or vulnerabilities. Issues that were identified but not addressed due to time constraints and other priorities. Interrupts These are tasks that disrupt the engineer's flow of work on their current project. While some interrupts are necessary, excessive or poorly managed interrupts can lead to significant productivity loss. Code reviews. While crucial for code quality, frequent or lengthy code reviews can interrupt development flow. Optimizing the review process (e.g., smaller, more frequent reviews, clear guidelines) can mitigate this.Design reviews. As teams continuously develop new features, frequent design review discussions are imperative.Ad hoc customer issues or support requests. These are unexpected problems reported by customers (or internal users) that require immediate investigation and resolution. They can range from minor bugs to critical production issues. Examples include bug fixes for released software, troubleshooting production problems, and answering support questions.Instant messages , meetings or emails requiring immediate attention. Notifications that distract engineers from focused work. Documentation Updates Keeping documentation up-to-date is essential for maintainability and knowledge sharing. However, documentation often falls behind during project development. Challenges Addressing non-project backlogs presents several key challenges for software engineering teams, spanning prioritization, management, technical considerations, and the impact of interruptions. 1. Prioritization and Time Allocation Balancing act. A core challenge is balancing the need to deliver features within project timelines with the need to address non-project backlogs. This balancing act often feels like a trade-off, leading to the consistent postponement of non-project work.Lack of clear ownership. When no one is explicitly responsible for a specific non-project task (e.g., updating a rarely used part of the documentation, fixing a minor bug in a legacy system), it tends to fall through the cracks. "That's not my job" becomes the unspoken sentiment, and the backlog item remains unresolved, potentially growing into a larger problem.Difficulty in estimating effort. Estimating the effort required for non-project tasks is often more challenging than estimating feature development. Refactoring a complex piece of code, for instance, might uncover unexpected dependencies or hidden complexities, making it difficult to predict how long it will actually take. This inaccurate estimation can lead to underestimation of the time needed, resulting in further delays and frustration. 2. Management and Visibility Hidden backlog. Non-project backlogs are often managed informally — as tacit team knowledge, fragmented documentation, or within archived correspondence. This impedes accurate assessment, effective prioritization, and consistent progress tracking.Lack of processes. Even when the backlog is acknowledged, teams may lack the necessary processes (e.g., regular backlog grooming sessions, defined workflows etc.) to manage it effectively. This can lead to chaos and inefficiency.Lack of management support. If management doesn't recognize the importance of addressing non-project backlog, they are unlikely to allocate time, resources, or budget for it. This lack of support can create a sense of futility among engineers, discouraging them from even attempting to tackle these issues. 3. Technical and Motivation Factors Technical complexity. Some non-project backlog work items, like refactoring a large, complex system or addressing deep-seated technical debt, can be technically challenging and require specialized knowledge. This complexity can make engineers hesitant to take on these tasks, especially if they lack the necessary expertise or feel overwhelmed by the scope of the work.Lack of motivation. Working on non-project backlogs is often perceived as less exciting than developing new features, feeling like maintenance work and demotivating engineers driven by building new things.Fear of breaking things. When dealing with legacy code or poorly documented systems, engineers may be afraid of introducing new bugs or breaking existing functionality. This fear can lead to procrastination or reluctance to make necessary changes, even if they are important for long-term maintainability. 4. Interruptions and Context Switching Constant interruptions. Frequent interruptions, such as urgent support requests, unexpected meetings, or constant messages, can disrupt engineers' flow and make it difficult to concentrate on any task, including non-project backlog. These interruptions fragment their workday, making it hard to make progress on complex tasks.Context switching overhead. Every time an engineer is interrupted and has to switch from one task to another, there is a "context switching" cost. This involves mentally unloading the current task and loading the new one, which takes time and mental effort. Frequent context switching can significantly reduce productivity and increase the risk of errors. By understanding these challenges, software engineering teams can develop more effective strategies for managing non-project backlog and mitigating its negative impact on their work. Implementation A structured plan is essential for effectively managing non-project backlogs, minimizing their impact on projects and improving overall efficiency and product quality. The following comprehensive plan incorporates best practices. 1. Assessment and Prioritization Triage and initial assessment. Create a comprehensive list of all non-project backlog work items. Use a tracking system to log each item with a clear description and impact to the business or process.Categorize. Group similar work items into categories such as technical debt, documentation, interrupts, support etc. This helps in understanding the overall nature of the backlog.Prioritize. Prioritize work items (bugs, etc.) using a framework (Must have, Should have, Nice to have) based on impact and effort. Consider factors like severity (critical vs. cosmetic), frequency, user/customer impact (or developer velocity impact), and risk of future issues. 2. Backlog Item Refinement Post-triage, a work item requires refinement to establish precise allocation requirements. This process can be facilitated by senior engineering staff through scheduled backlog refinement meetings or dedicated ad-hoc sessions. 3. Allocation and Execution Dedicated time. Allocate specific time slots (e.g., a portion of each sprint, a dedicated "maintenance sprint" every few sprints, or a specific day of the week) for addressing prioritized non-project backlog work items. This prevents these tasks from being constantly pushed aside by project work.Capacity planning. When planning sprints or iterations, explicitly allocate 10-15% capacity for non-project work. In addition to project story points, consider the estimated effort required for backlog work items.Assign ownership. Assign specific engineers or teams responsibility for addressing particular backlog work items. This ensures accountability and prevents work items from falling through the cracks.Integrate into workflow. Incorporate non-project backlog work items into the team's existing workflow (e.g., sprint planning, daily stand-ups, retrospectives). This makes them a regular part of the development process. 4. Monitoring and Review Track progress. Track metrics such as the number of completed backlog items, time spent on backlog work, and impact on project velocity.Regular reviews. Regular reviews, such as during sprint retrospectives, should be conducted to assess the effectiveness of the implementation plan.Continuous improvement. Treat managing non-project backlog as an ongoing process of continuous improvement. Regularly evaluate the team's practices and look for ways to optimize the process. 5. Process Improvements Reduce interrupts. Minimize interruptions through: clear communication guidelines (e.g., SLAs for code reviews), optimized meeting schedules (agendas, time limits, consolidated meeting days), and promoting focused work time (e.g., "do not disturb" modes, dedicated quiet periods like two meeting-free days per week).Improve documentation. Maintain up-to-date documentation by documenting during feature development and conducting regular reviews for accuracy and completeness.Address root causes. Investigate the root causes of recurring issues to prevent them and avoid operational toil, rather than just mitigating the immediate problem. For example, if ad-hoc customer issues are frequently related to a specific feature, consider refactoring or redesigning that feature. Example Implementation in a Sprint During sprint planning, the team allocates 10-15% of their sprint capacity to addressing top-priority non-project backlog work items.One or two engineers are assigned to work on specific technical debt or interrupt work items identified during the backlog prioritization.The team agrees to limit interruptions during designated "focus time" blocks.During the sprint retrospective, the team reviews the progress made on the backlog work items and discusses any challenges encountered. Conclusion In conclusion, effectively managing non-project backlogs (technical debt, interruptions, documentation, support) is essential for productive, high-quality software development. A structured approach — including assessment, prioritization, dedicated time, process improvements (reducing interruptions, better documentation), and continuous monitoring — is key. Ignoring this backlog slows development and risks escalating technical debt. Proactive management, however, yields a cleaner codebase, improved maintainability, fewer production issues, and greater focus on feature delivery. Consistent execution and continuous improvement transform the non-project backlog from a liability into an opportunity for long-term success. Start by assessing your current backlog and prioritizing the most impactful items.

TL; DR: The Alignment-to-Value Pipeline Effective product development requires both strategic alignment and healthy Product Backlog management. Misalignment leads to backlog bloat, trust erosion, and building the wrong products. By implementing proper alignment tools, separating discovery from delivery, and maintaining appropriate backlog size (3-6 sprints), teams can build products that truly matter. Success depends on trust, collaboration, risk navigation, and focusing on outcomes over outputs. Learn more about how to embrace the alignment-to-value pipeline and create your product operating model. Introduction: The Alignment-to-Value Pipeline Two critical challenges persist regardless of team experience or organizational maturity: creating meaningful alignment between stakeholders and teams, and maintaining a healthy, actionable Product Backlog. These challenges are fundamentally connected — alignment issues manifest as Product Backlog dysfunctions, you create things that do not solve your customers’ problems, and Product Backlog anti-patterns often signal deeper alignment problems. The following two graphics display the principle idea of the alignment-to-value pipeline: Alignment Tools Product Backlog Management The optimal flow from strategic alignment through product discovery and validation to delivery is not a linear process but a continuous cycle where each element reinforces the others: The first graphic shows how various alignment tools connect to different stages in the product development lifecycle, from strategy to tactics.The second graphic demonstrates how validated hypotheses flow from product discovery into the Product Backlog, while items deemed not valuable flow into an “Anti-Product Backlog.” The Cost of Failing the Alignment-to-Value Pipeline When alignment breaks down, the consequences cascade throughout the development process: Strategic disconnection. Without proper alignment tools, teams lose sight of why they’re building what they’re building, leading to feature factories prioritizing output over outcomes.Backlog bloat. Misalignment leads to Product Backlogs that become “storage for ideas” rather than actionable plans, creating a “collection of work items” — an expensive investment with quickly diminishing returns.Trust erosion. When stakeholders and teams operate from different understandings of goals, product value, and priorities, trust erodes and is replaced by micromanagement and control mechanisms.Validation bypass. Without alignment on what constitutes value, teams often skip proper validation, leading to mere busyness; “garbage in, garbage out” is real in product development. Insights into Bridging Alignment and Product Backlog 1. Separation of Discovery and Delivery There is a critical need to separate discovery from delivery while practicing them simultaneously. This separation is not about different teams but about different artifacts and processes. Product discovery artifacts (like Opportunity Canvas or Opportunity Solution Tree) help validate what’s worth building, while the Product Backlog contains only validated items ready for refinement and implementation. 2. The Right Size for the Right Action Excessive preparation is instead a hindrance rather than a benefit: Maintain just enough alignment and just enough Product Backlog to enable effective action without creating waste. The sweet spot appears to be 3-6 sprints of refined work aligned with clear strategic goals. 3. Empowerment Through Structure A seemingly paradoxical insight emerges: the right structures and tools enable greater empowerment and autonomy. Alignment tools provide frameworks that empower teams to make autonomous decisions aligned with organizational goals.Clear Product Backlog practices (like proper refinement and INVEST principles) empower Developers to challenge the Product Owner constructively. Jocko Willink refers to it as “discipline equals freedom,” or the dichotomy of leadership. 4. Balancing Technical and Business Concerns There is no way to avoid acknowledging the tension between business features and technical quality: While the business may push for delivering more features, the engineers are — at the same time — responsible for preserving the quality of the technology stack to ensure long-term technical viability and avoid technical debt running havoc. The alignment tools, particularly the Product Goal Canvas and Opportunity Solution Tree, provide frameworks to incorporate both business outcomes and technical quality into planning and prioritization. Practical Recommendations: Creating the Alignment-Backlog Connection Let us delve into a short list of conversation starters to create the vital alignment-backlog connection: 1. For Organizations Implement Dual-Track Agile Formalize the separation between discovery and delivery tracks while ensuring they inform each other continuously. Ideally, product teams do both in parallel. Adopt Strategic Alignment Tools Choose appropriate tools based on your context: For startups or new initiatives: Lean Canvas and Now-Next-Later Roadmap.For established products: Product Strategy Canvas and GO Product Roadmap.For all contexts: Regular alignment sessions using the selected tools; inspect and adapt apply as first principles here, too. Create Transparent Artifacts Ensure product roadmaps, strategic goals, and Product Backlogs are visible to everyone, helping everyone to understand “what they fight for.” Normalize Continuous Refinement Establish regular refinement as an organizational habit, not just a team activity. 2. For Product Owners Maintain an Anti-Product Backlog Explicitly track ideas considered but not pursued to avoid the “storage for ideas” Product Backlog anti-pattern. Limit Work in Progress Keep your Product Backlog small enough to be manageable (3-6 sprints worth) but comprehensive enough to guide development by providing the bigger picture. Balance Validation Methods Use proper tools for validation rather than prematurely adding items to the Product Backlog: Opportunity Canvas for understanding the problem space.Lean experiments for testing hypotheses.Usability testing for validating concepts. Employ Visual Management Visual tools like user story mapping create shared understanding across stakeholders and teams. 3. For Developers Demand Technical Excellence Allocate approximately 20% of capacity to preserve long-term technical quality by regularly tackling technical debt and quality improvements. Embrace Slack Time Request 20% of unplanned capacity to enable adaptation to operational challenges and innovation. Challenge Value Propositions Question why items are in the Product Backlog and if they best use the team’s time from a value creation perspective. Participate in Discovery Take active roles in the product discovery process rather than waiting for requirements. 4. For Scrum Teams as a Whole Regular Alignment Check-Ins Schedule dedicated sessions to revisit and update alignment tools, ensuring they reflect current understanding. Whole-Team Refinement Involve the entire Scrum team in refinement activities, avoiding the “involving the Scrum team — why?” anti-pattern. Balanced Refinement Time Invest appropriate time in refinement — neither too little (resulting in poor quality) nor too much (leading to analysis paralysis). Link Everything to Outcomes Connect all work items to specific, measurable outcomes using tools like the Opportunity Solution Tree. Reflection Questions on the Alignment-to-Value Pipeline Before starting a discussion in your organization about the alignment-to-value pipeline, ask yourself: Where is the line between product discovery and delivery in your organization? Are they separate processes with different artifacts, or are they blurred together?Which of the alignment tools mentioned would most benefit your current context, and why?What are the top three Product Backlog anti-patterns you observe in your organization, and how might better alignment tools address them?How might you implement the concept of an “Anti-Product Backlog” to track ideas considered but not pursued?Is your team allocating adequate time for technical excellence and slack time? If not, what could help make the case for this investment? Remember, achieving alignment is not about creating perfect documents or following processes rigidly. It’s about building shared understanding through conversations facilitated by appropriate tools. Also, maintaining a healthy Product Backlog is not about perfection but continuous improvement and adaptation. The more alignment you create upfront, the less waste you’ll generate downstream. And the healthier your Product Backlog, the more effectively you can deliver on the promise of that alignment. In other words, shift decisions on what to build left. Conclusion The journey from alignment to delivery is not a linear process but a continuous cycle. Alignment tools create the context for effective discovery, which feeds validated hypotheses into the Product Backlog. Proper Product Backlog management and refinement ensure the team builds the right things correctly, delivering increments that provide feedback for realignment. The success of this cycle depends on several critical factors: Trust – Between stakeholders and teams and among team members.Collaboration – Not just working together but true partnership in solving problems.Risk navigation – Using alignment and validation to reduce uncertainty.Value creation – Focusing consistently on outcomes over outputs. By integrating alignment practices with proper Product Backlog management, teams can avoid building products that technically meet specifications but fail to deliver real value — the build trap of the feature factory. Instead, they can create products that genuinely matter to users and organizations. How are you creating alignment? Please drop me a line or comment below.

By Stefan Wolpers CORE

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

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