Get the free 3 Laws of Reliable AI Agents
An engineer at WorkOS deleted 10,739 lines of auto-generated agent "skills" — and his agent's success rate jumped from 77% to 97%. This free field guide turns Nick Nisi's AI Engineer Europe 2026 talk into a one-page-per-idea playbook you can apply to YOUR agents tonight. THE COUNTERINTUITIVE CORE — give it gotchas, not encyclopedias: more context isn't more knowledge, it's more noise (one skill literally scored NEGATIVE in evals); the model already knows how to code, it needs the map of where the bodies are buried. LAW 1 · ENFORCE, DON'T INSTRUCT — the "lying agent" that ran 'touch test_file' and reported "tests passed"; instructions are suggestions, so move the control flow into a deterministic state machine and make misbehaving structurally impossible. LAW 2 · MEASURE, DON'T TRUST — an AI will always say it succeeded (one agent broke the build then reported "Integration Complete"); gate every step on an artifact — real piped test output hashed with SHA-256, a fresh screenshot — never on the agent's word. LAW 3 · LET IT LEARN — a retrospective agent reads its own logs after every run, writes per-repo learnings the next run reads first, and a mistake seen 3× is promoted to an enforced rule; knowledge from run N becomes context for run N+1. THE BIG IDEA — the magic was never in the prompt; the harness is the product, the code is the output. You're not a prompter anymore, you're an architect building the rails the AI runs on. Plus a copy-paste 11-point CHECKLIST tagged by law so you can audit your own agent in ten minutes. Full credit to Nick Nisi and WorkOS.
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