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AI Agent Accountability Comparison
Short answer: Accountability is a workflow, not one feature. Receipts support execution-level review, observability supports multi-step diagnosis, manual review supplies domain judgment, and structured evaluation checks declared expectations. The right combination depends on the consequence of the task.
Last reviewed: 2026-09-23. This page describes a reviewable evidence pattern and does not claim an outside result without upstream confirmation.
Comparison matrix
| Category | Useful for | Boundary |
|---|---|---|
| Execution receipts | Portable proof of one recorded call. | Cannot prove an unobserved outside result. |
| Observability | Tracing a multi-step run. | A trace may not be public or independently checkable. |
| Manual review | Context, judgment, and exceptions. | Not automatically reproducible or machine-verifiable. |
| Structured evaluation | Declared tests and expected properties. | A passing evaluation does not prove every production action. |
Open a public execution receipt or call its verifier. The example reports verification_status: verified for the stored record. It is not proof of an unobserved external outcome.
Frequently asked questions
AI agent accountability comparison
Accountability is a workflow, not one feature. Receipts support execution-level review, observability supports multi-step diagnosis, manual review supplies domain judgment, and structured evaluation checks declared expectations. The right combination depends on the consequence of the task.
What can a verifiable receipt prove?
It can show what the execution layer recorded and what its integrity checks verify. It cannot prove an unobserved outside outcome.
How can another reviewer check the record?
Open the public receipt, compare the tool, time, result, and status, then call the verifier. Keep outside confirmations separate from the execution record.