AI Agent Execution Guarantee

What a receipt supports

A reviewer can inspect the recorded execution, compare its fields, and call the verifier independently.

What it does not support

A receipt alone cannot guarantee delivery, payment, publication, approval, or another outside state.

How to report honestly

Use executed, failed, blocked, pending, and confirmed as distinct states and attach real upstream evidence to any confirmed claim.

Frequently asked questions

Who authored the AER-1 draft?

AI agent execution guarantee

An receipt can guarantee only what the execution layer actually controls and records: a stable record for a selected call, its observed result, and the verifier's integrity response when those fields are available. It cannot guarantee an outside outcome without confirmation from the system that owns that state.

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.

What this question can establish

The practical question behind AI Agent Execution Guarantee is whether the record supports the claim being reviewed. A reader wants to know which parts of an AI agent execution a receipt can support and which outcomes depend on another system. Separate the request, the execution, the system's observed output, and confirmation from any outside system. These are different evidence boundaries, and a reviewer should not treat them as interchangeable.

Use precise verbs such as requested, executed, recorded, pending, and confirmed. Reserve guarantee language for obligations actually defined elsewhere. A useful conclusion names the evidence that was checked and the conditions that remain unresolved. This keeps an execution record useful without stretching it into a broader assurance claim.

Evidence to retain for this workflow

Start with the smallest set of fields that lets a second reviewer identify the event and repeat the relevant check. For this topic, Identify what the execution layer controls, what it records, which status it returned, what bytes were committed, and whether a real upstream confirmation exists.

• Task boundary: record the requested state and what would count as acceptance.
• Execution identity: preserve the receipt ID, tool name, caller scope, time, and status.
• Committed result: keep canonical bytes or the published representation needed to recompute the output hash.
• Provenance: distinguish an action the system ran from one it observed or merely received as a report.
• External confirmation: capture a real callback, transaction reference, read-back, or provider record when the task depends on outside state.
• Review context: note who checked the record, which verifier was used, and any limitation that affected the conclusion.

A repeatable review sequence

Use the same order for routine checks and incident review. It prevents a later summary from replacing the evidence the task actually requires.

1. State the requirement. Write the expected outcome and the exact source that can confirm it. A reader wants to know which parts of an AI agent execution a receipt can support and which outcomes depend on another system.
2. Locate the execution. Match the public receipt identifier to the requested event, then confirm the tool, scope, timestamp, and status.
3. Check integrity. Decode the committed bytes as specified by the receipt format, recompute fingerprint, and compare the complete digest rather than a shortened preview.
4. Check the owning system. When the claim involves an outside change, query that system or inspect its genuine callback. A receipt alone cannot supply a missing confirmation.
5. Write a bounded finding. Record what ran, what was observed, whether the bytes matched, and what remains unknown. Use precise verbs such as requested, executed, recorded, pending, and confirmed. Reserve guarantee language for obligations actually defined elsewhere.

Failure states and misleading success signals

Preserve failed, blocked, pending, retried, and partially completed states as distinct outcomes. A receipt cannot create a guarantee about delivery, approval, payment, publication, or availability when those events are outside its observation boundary. A successful transport response can still contain a tool error, and a valid integrity check can still protect an incorrect or incomplete result.

When evidence conflicts, keep both records and identify which system owns each fact. Do not overwrite an earlier receipt or silently promote a retry into proof that the original step succeeded. Explain whether the conflict is a timing issue, a stale read, a missing callback, a policy boundary, or a different task interpretation.

Privacy, retention, and safe sharing

Reviewability does not require publishing every input. Publish only a receipt projection suitable for the claim. Keep private inputs, account data, and credentials out of public records. Prefer stable references and verifiable commitments where they are enough for the reviewer, and keep the underlying sensitive record with the system that controls it.

Before sharing, check the public page, result preview, metadata, and linked evidence for secrets or personal information. Set retention and access according to the review purpose. If a record must be corrected, publish a new reference and preserve the original history rather than rewriting a record that another reviewer may already have captured.

Example of a careful conclusion

This kind of note gives the next person a useful handoff: what was checked, what the check means, and where the evidence stops. It is more informative than a bare success label and avoids presenting an unobserved event as a verified fact.

Common review questions

What does the record establish?

It establishes what the execution system recorded and whether the exposed canonical bytes match the output commitment. For ai agent execution guarantee, it does not by itself establish correctness or a result outside that system.

What if a provider or callback is unavailable?

Keep the state pending or unavailable, retain the error and time, and avoid claiming completion. In this workflow, A receipt cannot create a guarantee about delivery, approval, payment, publication, or availability when those events are outside its observation boundary.

What should a reviewer inspect first?

Identify what the execution layer controls, what it records, which status it returned, what bytes were committed, and whether a real upstream confirmation exists. Then compare the record with the task requirement and ask the system that owns any claimed external state for a separate read-back.

What is safe to share?

Share the public receipt URL, identifier, status, verifier result, and a short sanitized task description. Publish only a receipt projection suitable for the claim. Keep private inputs, account data, and credentials out of public records.

Further reading

• Trust and verification
• Reviewing agent work
• Task completion checks
• Agent handoffs
• Diagnosing incomplete work
• receipt definition