In this article, I want to take a closer look at the pitfalls of popular SaaS scaling strategies, drawing on my own experience, and share the lessons learned.
A real Gemini Pro session shows how AI can look “done” while skipping uploaded data and ignoring STOP commands — creating silent failure risk without verification.
Cloud systems drift when exceptions accumulate, and decisions lose connection to original objectives. Clear requirements and early security design prevent sprawl.
AI-driven development is outpacing security teams. This piece examines where AI-powered security actually help, where they fail, and how teams can use them responsibly.
Microservices introduce distributed-systems complexity most teams underestimate: failures, coordination drag, observability sprawl, and ballooning costs.
Agent identity and its audit history will enforce zero-trust access for agents based on both identity and past behavior. This makes agent access more secure and reliable.
Retrieval-Augmented Generation (RAG) is transforming enterprise AI by bridging the gap between general-purpose language models and organization-specific knowledge.
Keep GenAI cheap and fast: cache aggressively, route models by confidence, cap tokens and tools, compress context, and monitor cost per successful outcome.
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