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AI engineering insights from the field — LLMs, agents, and production lessons.
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Field Notes
Too Many Tools Hurt Agents
Giving an agent every tool you own makes it worse at choosing one. The measured fix is a shortlist: keep a small core resident, retrieve the rest per request, and spend the saved context on the work itself.
Field Notes
Structured Output Is Not a Guarantee
Schema-constrained generation guarantees the shape of a model's answer — every required field present, every type right, every enum legal. It says nothing about whether the values are true. Production systems still need semantic validation, bounded repair, and a reject path.
Field Notes
Multi-Agent Systems in Production
Multi-agent systems earn their cost in one shape of work — breadth-first, read-heavy, loosely coupled — and lose it in another. The evidence from the teams and researchers who have measured them points to the same dividing line: fan out for reading, keep writing and deciding in one thread.
Field Notes
Long-Horizon Agents Need a Map, Not a Bigger Context Window
Long tasks rarely fail because an agent ran out of room to remember. They fail because everything it remembers arrives as one undifferentiated transcript. A small, structured map — goal, position, decisions, open ends, pointers — keeps a long run oriented long after a bigger window stops helping.
Network
The FDE Shortage Is a Proof Problem
Amazon's AWS calls qualified forward-deployed engineers "unicorns," and it is spending $1 billion to build an FDE organization anyway. The scarcity is real, but the missing piece is not a bigger applicant pool — it is proof. Trida AI's Academy certifies engineers against the skills the role demands, and the Network matches them to companies by verified skill.
Blueprints
Insurance Cash Reconciliation Agent Blueprint
Blueprint: an insurance cash reconciliation agent that matches premium cash to what is owed across broker netting, lump sums and currencies, holds what it cannot explain in suspense, and stops at a named controller who approves every posting.
Field Notes
Text-to-SQL Against a Real Schema: Where the Demo Database Stops Telling the Truth
We asked the same twelve business questions of the same data stored two ways, a clean demo schema and a legacy one, across three production models and two datasets. On the larger legacy database the best score was 3 of 12. Every failure clustered on facts the schema never states, and the fixes the post recommends lifted every model to 8 or better.
Field Notes
The First Two Weeks Decide
A deployment rarely fails in the build. It fails earlier, in the opening weeks, when the brief's version of the problem goes unchallenged. Five heuristics for the embed phase: find the real problem, the real data and the real owner before the build locks in.
Field Notes
Data Residency in AI Deployment
Where your data sits is the easy question. Where it travels — to inference, embeddings, logs and backups — is what decides whether an AI deployment passes your security review.