A question for engineers building AI agents that actually change things.

Imagine this fictional workflow:

  1. An agent is approved to update an ERP record.
  2. The role, task scope, and review binding pass preflight.
  3. The tool definition changes before dispatch.
  4. The gateway forwards the operation, but the client times out.
  5. Another agent takes over and considers retrying.

What evidence would you need before trusting the result?

I'm exploring four possible ingredients for a minimal evidence chain:

  • The exact approved task/action digest
  • The authenticated executor and effective tool version
  • The logical operation ID and retry identity
  • A fresh, authoritative readback of the actual result

But every extra receipt has a cost: storage, implementation, maintenance, and operational complexity.

I maintain Agent Role Contracts (ARC), an open-source project that checks supplied role, scope, review, and task/action declarations offline. Runtime enforcement and independent verification of external effects are separate responsibilities.

We're investigating these boundaries through a research pilot, with the real-world validation stages explicitly tracked.

My question: What's the smallest evidence chain you would trust in production?

Have you solved this with database logs, idempotency keys, event sourcing, workflow engines, or something simpler?

A real example would be especially useful:

system | side effect | failure | trusted evidence | retry rule | cost

I'm collecting concrete engineering perspectives in this GitHub discussion as well.