The Tool Desk
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What “done” does—and does not—tell you
An agent’s final message reflects what it believes happened. It does not establish that a tool ran successfully, that the destination accepted the change, or that the change remains in effect. A tool call can fail, return an unexpected result, or succeed while the agent’s response is interrupted.
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Keep two questions separate: Was the action allowed to run? Approval controls answer that. What actually happened? Execution evidence and a read of the destination system answer that. Neither approval nor a confident final response substitutes for checking state.
How to verify an agent’s work
Use this sequence for an action that updates a record, sends a message, changes a file, or affects another external system.
#1 Best Overall
- Define the expected change. Name the record, file, message, or other object and the specific result you expect. Identify the system that owns that state.
- Check the run or session status. Determine whether execution completed, failed, or was interrupted. Do not treat an error event or disconnected stream as a complete account of the outcome.
- Inspect the trace or equivalent execution record. Look for the relevant model and tool calls, handoffs, and guardrail events. Confirm the tool invocation occurred and inspect its inputs and outputs. OpenAI describes traces as end-to-end records of these execution steps; Microsoft likewise recommends checking expected calls and their inputs and outputs. OpenAI’s agent evaluation guidance and Microsoft’s tracing guidance explain these records.
- Read the destination system. Independently query or inspect the relevant state—for example, reload the record or check the sent-message folder—and compare it with the expected result. A trace shows what the agent attempted and what tools returned; it does not, by itself, prove that the intended state persisted.
- Decide what remains unfinished. If the result is uncertain, inspect completed effects before retrying. Resume or repeat only work that has not already taken effect.
Why an error or timeout is not a safe retry signal
A failed turn can still leave side effects. A tool may have updated a system before a later step failed, or the response stream may have disconnected after the action completed. OpenAI’s Errors and recovery documentation states: “An error event or a disconnected stream doesn’t confirm the turn’s final state.” It directs developers to check completed actions before recovery.
Blindly replaying the whole request can send a duplicate message, create a second record, or conflict with an update that already landed. First examine the execution record and destination state; then retry only the missing action. Where possible, design side-effecting operations to tolerate retries safely, but do not assume that a tool or destination provides that protection unless its behavior is established.
Put approval at the side-effect boundary
Verification is a post-action check. Approval is a pre-action control. For consequential operations, pause immediately before the tool call that can cause the side effect, present the action and its relevant details for review, and continue only after the decision. OpenAI’s SDK approvals guidance describes interrupting execution for approval and resuming the same run afterward; it also recommends placing validation next to a custom tool when every call needs checks.
A prompt that asks the agent to seek confirmation is not necessarily an enforcement mechanism. If the application depends on the agent choosing to call a confirmation function, the agent can fail to make that call. OpenAI cautions in its computer-use documentation: “Origin approval does not enforce confirmation before individual actions.” Granting access to a site is therefore distinct from requiring review before a purchase, deletion, or other consequential operation. If review must be guaranteed, enforce it in the application or runtime at the action boundary rather than relying on the agent’s conversational behavior.
Rank #3
When the application predicts state changes
Some agent interfaces show a proposed change before committing it. Treat that proposal as predicted state, not applied state. Microsoft’s AG-UI confirmation guidance supports requiring client confirmation before predicted changes are applied; its example discards rejected predictions. The application should make the transition explicit: proposed, approved, applied, and verified are different states.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make verification repeatable
A one-off manual check can catch an incident; repeatable evaluation helps reveal regressions as prompts, tools, models, or application logic change. OpenAI recommends using traces and graders to debug agent behavior, then datasets and repeatable evaluation runs for benchmarks and comparisons. Microsoft recommends evaluating before publishing and after meaningful changes, followed by production monitoring of quality and safety signals. See OpenAI’s agent evaluation guide and Microsoft’s evaluation guidance.
Turn failures and near misses into evaluation cases: record the expected destination state, the tool activity that should occur, what must require approval, and how recovery should behave if execution stops partway through. Use traces to diagnose the run and a separate destination-state check to judge whether the outcome was correct. These sources describe platform guidance and practices; they do not establish a universal success rate or prove that one verification design works best across all agent systems.
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Design questions for an agent workflow
- Enforcement point: Is validation attached to the tool that causes the side effect, or only expressed as an instruction to the agent?
- Evidence: Can an operator inspect tool inputs and outputs, and is there a separate read of persisted destination state?
- Consequential actions: Does execution pause for review and resume only after an explicit decision?
- Failure recovery: Does the workflow check for completed effects before retrying?
- Repeatability: Can the team replay evaluation cases and compare behavior across changes?
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