Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesAn agent’s “done” is a claim, not a result. The fix is a small gate that sits between the agent’s final message and your acceptance of the work. It checks the deliverable against criteria written in advance, looks at how the agent got there, and treats missing evidence as “not verified.” This guide is an instructional account built from published guidance by OpenAI, Anthropic, Microsoft and Google Cloud. It isn’t a report of one team’s measured results, and none of those sources gives a failure rate or an improvement figure.
The gate in six steps
- Specify success before the run. Turn the request into checkable acceptance criteria: required files or state changes, constraints, expected tool effects, and how the deliverable will be judged.
- Check the result, not the message. Run deterministic assertions or task-specific tests against the artifact or environment state the agent produced.
- Inspect execution evidence. Read the trace for tool choice, argument validity, tool success, correct use of returned data, and required handoffs.
- Fail closed. A missing artifact, failed check, incomplete trace or unmet criterion means “not verified.” Require a repair or human review. This rule is my inference from the documented checks, not a vendor requirement.
- Repeat against a fixed set. Keep representative tasks and rerun them when prompts, models, tools or routing change.
- Test at the right boundary. Use in-memory tests for orchestration you own, and integration environments for behavior owned by external systems.
None of this is clever, which is the point. Each step turns “the agent says it finished” into evidence you can inspect.
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Why the final message isn’t enough
Anthropic defines an evaluation this way: “An evaluation (“eval”) is a test for an AI system: give an AI an input, then apply grading logic to its output to measure success.” In its guidance on evals for agents, it describes multi-turn runs that use tools and change an environment. Mistakes can carry forward from one turn to the next, so a confident last message can sit on top of an earlier error. In its coding-agent example, unit tests verify the implemented result. They don’t rely on the agent’s description of it.
Google Cloud’s Hugo Selbie, a Staff Customer & Partner Solutions Engineer, makes the same point in a November 17, 2025 article: “Metrics focused only on the final output are no longer enough for systems that make a sequence of decisions.” He warns of “silent failure,” where a correct-looking result came from an incorrect process. That is a vendor practitioner’s argument, not a controlled comparison. It does describe a real failure mode that you can test for.
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Step 1: Write criteria that can be checked
“Fix the bug” can’t be graded. “The failing test passes, no other test regresses, and only files under src/billing/ change” can. For each task, write down:
- the artifacts or state changes that must exist
- constraints, such as files that must not be touched or policies that must hold
- which tool effects are expected, and which are forbidden
- how the result will be judged: assertion, rubric or human review
OpenAI’s evaluation best practices list the kinds of checks worth specifying: instruction following, functional correctness, tool selection, argument accuracy and handoff accuracy.
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Step 2: Check the deliverable
Where the outcome is objective, use executable checks. Examples are tests that pass, a file that parses, a record that exists in the right state, or a build that succeeds. Where quality is subjective, such as tone, summary quality or design, a binary test won’t capture it. Use a rubric-based grader or a human reviewer. OpenAI’s agent evaluation guide describes graders for structured scoring. Microsoft’s evaluator descriptions pose the central question directly: did the agent fully complete the requested task?
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OpenAI’s documentation says: “A trace captures the end-to-end record of model calls, tool calls, guardrails, and handoffs for one run.” The guide frames the questions you ask of it in plain terms: did the agent pick the right tool, and did a handoff happen when it should have?
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Microsoft Foundry’s agent evaluators split the work into two layers:
| Layer | What it asks | Examples from Microsoft’s docs |
|---|---|---|
| System evaluation | Did the whole run meet the goal? | Task completion, instruction adherence |
| Process evaluation | Was the path sound? | Tool selection, input accuracy, tool success, correct use of tool outputs |
Microsoft labels some of these evaluators as preview, so check their current status before building on them. Even without that platform, the same questions work as a manual trace checklist. Was the right tool called, with valid arguments? Did it succeed? Did the agent actually use what came back? Did required handoffs happen? Google Cloud’s three pillars cover the same ground from another angle: outcome and quality, process and trajectory, and trust and safety under non-ideal conditions.
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Step 4: Fail closed
The gate should default to rejection. Treat these as “not verified,” not as “probably fine”:
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- any deterministic check fails
- the trace is incomplete or can’t be retrieved
- a criterion has no evidence either way
The next action is then a repair attempt or human review. A “done” message alone never moves the task forward.
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Step 5: Repeat against a fixed set
One passing run is weak evidence, because model outputs vary. Anthropic cites that variation as a reason to run multiple trials. OpenAI advises moving from ad hoc trace inspection to datasets and evaluation runs when you need repeatable benchmarks or prompt comparisons. A workable split:
- Traces for diagnosing why one run failed.
- A fixed task set for comparing before and after a change to a prompt, model, tool or routing rule.
The sources give no universal pass threshold and no magic number of trials. Choose both from the task’s risk and the variation you observe. A low-stakes formatting task and a task that moves money warrant different bars.
Step 6: Test at the right boundary
The OpenAI Agents SDK testing documentation describes deterministic, provider-neutral utilities that run in memory without calling model or sandbox-provider APIs. It recommends them for behavior your application or the SDK owns: tool execution, handoffs, guardrails, retries and workflow drift. For behavior owned by external systems, such as a model, network, sandbox or audio, it advises real adapters or integration environments. That keeps the fast, cheap checks deterministic and puts the realistic checks where they belong.
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Choosing the right check
| Axis | One side | Other side |
|---|---|---|
| Outcome vs. process | Is the deliverable usable and does it meet requirements? | Was the execution path and tool behavior correct? |
| Deterministic vs. judgment | Executable assertions | Graders or expert review for subjective qualities |
| Owned vs. external | In-memory workflow tests | Provider or integration tests |
| Debugging vs. regression | Individual traces | Fixed datasets and repeated runs |
| Efficiency vs. correctness | Fewer, cleaner steps | Task success and robustness |
These are complementary, not competing products. They are my synthesis of the sources, not an official standard. Efficiency is worth tracking, but it shouldn’t substitute for task success. OpenAI also says evaluation results should guide whether a multi-agent architecture is warranted at all. If a handoff-heavy design doesn’t score better, simplify it.
The Bottom Line
<p>Write the criteria first, check the artifact and the trace, and reject anything you can’t verify. Then rerun a fixed task set whenever something changes. Pick the thresholds yourself, based on risk, because no source supplies one.</p>
Quick Recap
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