Notes on building AI systems that have to work in production — mostly the unglamorous parts. Written from the perspective of someone who spent 17 years on enterprise backends before the current wave.
RAG is not one architecture, it is at least seven — and they fail in different ways. A field guide to the retrieval patterns that actually ship: what each one costs, what it fixes, and how the shape of your users' questions decides which one you need.
Read →I lead development of an agentic AI platform, which is exactly why I spend so much time talking people out of building one. A four-rung ladder from plain code to a full agent loop — and the one question that decides which rung your problem belongs on.
Read →Everyone's first agent is twenty lines: call the model, run the tool, append the result, repeat. What has to go in between those steps — validation, tool scoping, approvals, bounded execution, re-validation — before it survives real users.
Read →Before I worked on agents, I spent a year building business activity monitoring for a national telecom. Almost everything useful I know about debugging LLM systems, I learned there — and the industry is currently relearning it the hard way.
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