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I've been spending weekends thinking about authorization for AI agents, specifically delegation.

The failure mode I keep hitting: once you give an agent tools, it gets ambient authority over all of them. There's no clean way to say "for this task, read-only on the reports table" or "spin up no more than 3 VMs." When the agent spawns sub-agents mid-execution, they inherit full access by default.

IAM doesn't help much. Authority stays tied to the agent's identity even as intent shifts during execution.

I'm exploring a capability-based model instead: authority is explicit, task-scoped, and attenuating. Closest to Macaroons/Biscuit, but adapted for workflows where delegation happens dynamically mid-task.

Early prototype (Rust core, Python SDK, LangChain integration), still thinking it through. Notes here: https://niyikiza.com/posts/capability-delegation/





Excellent article, and I fully agree.

I came to the same realization a while ago and started building an agent runtime designed to ensure all (I/O) effects are capability bound and validated by policies, while also allowing the agent to modify itself.

https://github.com/smartcomputer-ai/agent-os/


Thanks! Just looked at Agent OS. Love the 'Signed Receipts' concept in your AIR spec.

We reached the same conclusion on the 'Ambient Authority' problem, but I attacked it from the other end of the stack.

Tenuo is just the authorization primitive (attenuating warrants + verification), not the full runtime. The idea is you plug it into whatever runtime you're already using (LangChain, LangGraph, your own).

I'm currently in stealth-ish/private alpha, but the architecture is designed to be 'userspace' agnostic. I’d love to see if Tenuo’s warrant logic could eventually serve as a primitive inside an Agent OS process.

I'll shoot you a note. I would love to swap notes on the 'Capabilities vs. Guardrails' implementation details.




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