I’ve Started Writing an Open Book on Secure AI Agent Architecture
I’ve started writing an open book on the architecture of secure AI agents.
The goal is to build a practical engineering reference — not a collection of flashy demos, but a structured guide to production-grade agent systems: control planes, policy boundaries, tool execution, memory, observability, evaluations, approvals, and governance.
The first chapters are already live:
- English: https://agent-axiom.github.io/agent-arch/en/
- Chinese: https://agent-axiom.github.io/agent-arch/zh/
Repository: https://github.com/agent-axiom/agent-arch
There is a lot of excitement around agents, but far less shared engineering guidance on how to build them safely and operate them reliably in production. This project is my attempt to help close that gap.
I’d genuinely appreciate thoughtful feedback from the community:
- what feels solid
- what is missing
- what seems debatable
- what should be improved
- what operational or security practices deserve more attention
If this topic is close to your work, I’d be glad to hear your critique, ideas, counterexamples, and contributions.
Top comments (1)
The control-plane framing is a good foundation. One artifact that would make the book especially actionable is a threat-model template per tool: caller identity, capability scope, data classification, approval point, and the audit evidence required after execution. Then teams can review a new connector as a concrete policy diff instead of rereading the whole architecture.