Operating AI in production.
Evergreen chapters for people putting AI into production — why governance matters, how evidence differs from logs, how to think about agent identity, and why production AI needs a different operational model than the software before it.
AI agents are becoming production software. Production software needs governance.
Start here
The ideas that define the categoryReplay
AI systems rarely fail the same way twice. Reconstructing what happened is often harder than fixing it.
Read the chapterEvidence
The day someone stops trusting your word, logs become surprisingly useless.
Read the chapterReceipts
Every meaningful action deserves a small, signed record. Most systems still leave prose.
Read the chapterAuthorities
On whose authority did the agent act? "The model" is not a defensible answer.
Read the chapterGovern
Rules an AI agent will actually follow, not policies buried in a wiki.
Read the chapterRun
Every decision needs a place that can say allow, ask, or deny — in microseconds, in the hot path.
Read the chapterProve
Anyone should be able to verify a past decision without your help.
Read the chapterAlso in the guide
Implementation ideas- Decisions
The business action above the tool call: participants in, one verdict out.
- Policies
How rules compile into portable, versioned bundles.
- Identity
Agents aren't users and aren't services — how to think about non-human callers.
- Delegation
Scoped, time-boxed authority passed from one party to another.
- Verification
How a stranger checks a receipt bundle without access to your system.
- Kill switch
Stopping a class of decisions instantly without stopping everything.
- Local-first
Why keeping credentials and history on-device changes the trust model.
- Governance vs security
Security stops attackers. Governance decides what your own agents may do.
- Glossary
The five nouns we lean on: Receipt, Authority, Evidence, Policy, Identity.
Looking for the how, not the why? Go to Docs.