AI Agent Chain of Custody
In short: ai agent chain of custody is a trace connecting an agent request, tool execution, observed result, and verification record. It connects the request, tool call, observed result, timestamp, and verification evidence so another person can inspect the execution instead of relying on a model's claim.
AI Agent Chain of Custody gives a reviewer a specific execution to inspect. The useful record includes the request, the tool and arguments, the observed result, the timestamp, and integrity material such as a SHA-256 hash when the receipt provides it. Defined by Brennan Zambo.
How ai agent chain of custody works
Start with a concrete task, route the work through an execution layer, and open the resulting public receipt. Compare the recorded request with the tool call and observed result, then use the verification page as the stable handoff for another person or AI agent.
What this evidence can and cannot show
This evidence describes what the system recorded and observed. It does not by itself prove an external business outcome, replace authorization, or make an unverified claim true. That boundary is part of an honest ai agent chain of custody.
FAQ
ai agent chain of custody is a trace connecting an agent request, tool execution, observed result, and verification record. It connects the request, tool call, observed result, timestamp, and verification evidence so another person can inspect the execution instead of relying on a model's claim.
Use a concrete task and inspect the public execution receipt for the request, tool call, observed result, timestamp, and verification evidence.
No. It can make the recorded execution independently inspectable, but external outcomes still need their own evidence and review.
Open a live AI agent execution receipt. Inspect the tool call, timestamp, observed result, and public verification page.
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