Azure AI Foundry agent logging plus checkable receipts

The direct answer

Azure AI Foundry agent logging plus checkable receipts is a record of activity in a particular platform or process. Platform logs are useful for operations and investigations; a portable receipt adds a focused link to one observed tool call. Use both when you need operational context and an independently checkable handoff.

For azure ai foundry agent logging, choose one question that a reviewer can answer from the record. Name the tool or source, the exact action, and the expected output before doing the work. This makes it easier to see whether the result actually answers the question or merely sounds plausible.

A practical way to apply this

1. Write the user's intended outcome in one sentence.
2. Identify which source, MCP tool, platform log, or human review can answer the question.
3. Run the smallest safe action and preserve its returned status, timestamp, and receipt link.
4. Check the receipt independently and label any evidence that is missing or outside the captured boundary.

Keep the system's native logs for broad operational questions such as identity, permissions, deployment, or policy changes. Use a receipt for a specific AI tool call that needs to be handed to a reviewer. Link the two records when possible, but preserve which system created each one and the time range it covers.

A concrete example

A real example is this public read-only BTC lookup receipt: https://zambo.dev/run/83ae4fd7-b9f8-497c-b842-72381c8ee6a8. Its verifier is https://zambo.dev/api/v2/receipt/83ae4fd7-b9f8-497c-b842-72381c8ee6a8/verify. The example shows one observed tool result; it is not evidence about azure ai foundry agent logging in your own system.

For a work-specific review, replace the sample with the receipt generated by your own call. Match the tool name and receipt ID exactly. If the output is a proposal, a blocked action, or an unavailable response, retain that state instead of describing it as completed work.

What the record can and cannot tell you

Start with the platform's own event log for identity, permissions, and system changes. Then attach the relevant receipt links for specific tool executions. This gives an operator both the wide operational timeline and a narrow record that can be checked without recreating the whole agent session.

A checkable receipt records what the connected execution service saved. It does not establish that a model's complete answer is accurate, that every possible input was tested, or that an outside service changed state. When an outside effect matters, look for a confirmation from the system that owns that state, such as a read-back, callback, or provider reference.

For sensitive work, publish only the fields needed for review. Keep access tokens, private credentials, customer data, and unrelated chat context out of a public handoff. Explain what the receipt covers and what remains outside its scope.

Review checklist

• Does the record identify the specific request and tool?
• Does the receipt ID match the result being discussed?
• Can a reviewer reach the verifier without the original chat?
• Are failures, retries, and pending steps represented honestly?
• Is outside confirmation linked to its actual source?
• Are private values excluded from the public copy?

Use this checklist for azure ai foundry agent logging plus checkable receipts and save only the evidence relevant to the decision. A short, well-scoped record is easier to review than a large transcript with no clear boundary.