Engineering perspectives on AI agents, intelligent automation and enterprise AI. Practical thinking from the work of building production AI systems.
Production AI agents fail for different reasons than demos do. This piece covers the architecture decisions — task decomposition, tool design, error handling — that separate robust agents from fragile ones.
Model Context Protocol changes how AI agents interact with external systems. We look at what MCP actually solves, where it fits in an agent architecture, and practical implementation patterns.
Pilot-to-production failure is the defining problem in enterprise AI right now. We examine the common failure modes — scoping, data access, edge cases, user adoption — and how to avoid them.
Retrieval-Augmented Generation is not a single pattern. This covers the chunking, embedding, retrieval and evaluation decisions that determine whether an enterprise RAG system actually works.
AI-assisted development is real and useful — but code that works in a prototype often has structural problems that compound at scale. We cover the review patterns that matter most.
Autonomous agents are useful precisely because they act without constant supervision. But the right architecture defines exactly where human approval is required — and builds those gates deliberately.
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