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Before releasing a vertical agent, test whether it follows the rules that define its job across the full workflow—not just whether its final answer sounds convincing. A useful release test checks the answer, the tools and arguments used, any handoff, policy boundaries, and the actual result when the agent changes something.
What does it mean for an agent to keep its rules?
Start by turning the agent’s job description into observable behavior. Write down the work it is meant to handle, the domain rules it must follow, what it must not do, what information it can rely on, any required disclosures or citations, and when it must escalate to a person.
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Replace vague goals such as “give a good answer” with atomic assertions that can be checked. For each important scenario, record:
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- Scenario and prompt: A realistic user request, with representative details and data.
- Expected response or action: What the agent should say or do, including an acceptable “I don’t know” or escalation where appropriate.
- Pass evidence: The required facts, prohibited content, grounding or citation behavior, permission boundary, tool and arguments, handoff, or resulting state that demonstrates success.
- Grading method: How the assertion will be checked and what counts as failure.
For a single-turn test, keep the prompt focused on one intent so a failure is easier to diagnose. Microsoft Learn recommends beginning with one prompt for each key scenario and refining the acceptance criteria as the team reviews results. Treat that as a starting point, not a universal sample-size rule.
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Make unknowns and boundaries testable
Include questions the agent cannot answer from its approved knowledge. For example, Microsoft’s agent-evaluation guidance contrasts an answer grounded in a source with a question such as “How many sick leave days does an employee get?” when the source does not specify the allowance. In the latter case, the expected behavior is to say the information is unavailable and direct the user to human support—not invent a number.
Be equally explicit about prohibited actions, permission limits, required disclosures, and escalation conditions. A boundary that is only described in a prompt but never represented in a test is difficult to verify before release.
Which scenarios belong in the test set?
Organize tests by the kinds of behavior the agent must demonstrate. Use realistic names, records, and values; vary them enough to catch brittle rules, but keep the expected result clear.
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|---|---|---|
| Core work | Common in-scope requests using representative data and entity names. | Required facts or actions are correct and the request stays within the agent’s remit. |
| Knowledge and grounding | Answerable questions; missing, conflicting, stale, or irrelevant information; and attribution requirements where applicable. | The answer is supported by the available source, or the agent acknowledges the gap instead of guessing. |
| Instruction priority | Requests from users, or content from untrusted sources, that try to override the agent’s operating instructions. | The agent preserves its governing rules and does not treat untrusted content as authority. |
| Scope and escalation | Out-of-scope requests, unavailable facts, restricted permissions, and situations that require a person. | A suitable refusal, limitation, or handoff occurs instead of an unsupported answer or unauthorized action. |
| Tools and actions | Correct tool choice, exact argument extraction, preconditions, tool errors, retries, fallbacks, and disallowed state changes. | Tool calls and arguments are appropriate, errors are handled safely, and unauthorized changes do not occur. |
| Handoffs | Cases that must reach a particular specialist, plus the point at which the agent should resume or stop. | The request reaches the right destination and control returns as intended. |
| Variations and edge cases | Alternative phrasing, incomplete inputs, boundary values, and multi-turn conversations. | The required behavior holds across variations, or the agent asks for the information it needs. |
| Adversarial and loophole cases | Inputs designed to exploit the evaluator, benchmark rules, or tool environment. | The agent follows the intended policy rather than a shortcut that merely satisfies a visible score. |
Microsoft’s evaluation guidance calls out architecture checks such as tool and action use, retrieval and citation, routing, and handoffs, along with guardrail and out-of-scope edge cases. OpenAI’s evaluation examples include instruction following, tool selection, argument precision, functional correctness, and handoff accuracy. NIST CAISI has also documented how agents can exploit benchmark loopholes, making adversarial cases and transcript review important parts of evaluation.
How do you test the whole workflow, not just the final answer?
Inspect the execution trace: the sequence of model calls, tool calls, guardrails, and handoffs that produced the result. A polished response can conceal a wrong tool choice, malformed arguments, a missed permission check, or a handoff that never happened.
- Check whether the agent selected the right tool for the task.
- Compare tool arguments with the values in the user’s request and the tool’s requirements.
- Verify that required policy checks occurred before a consequential action.
- Confirm that a required handoff reached the correct destination.
- Review tool errors, retries, and fallbacks to ensure the agent did not claim success after a failed operation.
For any task that changes state, verify the state itself. An agent saying that it booked a flight is not proof that a reservation exists in the database. The same principle applies to a vertical agent that places an order, updates a record, approves a request, or sends a message: inspect the authoritative system for the resulting change.
OpenAI recommends an evaluation-driven development approach: write scoped tests early and evaluate frequently. In practice, that means saving cases and traces so a prompt, routing, or tool change can be checked against the same expectations rather than judged from a few new examples.
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Agent outputs can vary between executions. Anthropic describes each execution as a trial and recommends running multiple trials for more consistent results. A single pass or failure may be a useful signal, but it is not a reliable estimate of behavior across repeated use.
Use automated grading for repeatable checks, but validate the grader as well as the agent. Have knowledgeable reviewers read transcripts to find real failures, mistaken grades, and cases where the test itself is unclear. If an LLM judge is used, compare its decisions with human expert judgments and allow it to abstain when the available evidence is insufficient.
Review for shortcut behavior, too. NIST CAISI recommends examining evaluation transcripts for cheating and closing loopholes with clear task rules and standardized expectations for tool permissions and restrictions. A score is meaningful only if the agent passes by doing the intended task under the intended rules.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should a pre-release baseline measure?
Keep results separate for the dimensions that matter to the agent’s use case. A single aggregate score can hide a serious failure in a high-consequence behavior.
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- Rule adherence and policy boundaries.
- Answer correctness and grounding.
- Tool selection and argument accuracy.
- Handoff behavior.
- Actual task outcome and resulting state.
- Robustness across phrasing, edge cases, and repeated trials.
- Operational performance, including latency, throughput, and reliability.
Microsoft lists performance testing for latency, throughput, and reliability as complementary to agent evaluation. Set release criteria in proportion to risk: critical rules and high-consequence actions need explicit, stringent pass conditions, while lower-risk quality dimensions can have different targets.
Use published figures as guidance, not certification
Microsoft Learn’s overview, which gives no publication year on the page, recommends 20–50 test cases for a prototype, 50–100 for preproduction, and 100 or more for production. It also suggests aiming for an 80–90% overall pass rate, approaching 100% consistency on core regression tests, and running evaluations multiple times to account for variability. These are that page’s recommendations, not an industry-wide standard or a certification of readiness.
The same overview illustrates a report in which policy accuracy moves from 87% to 96%. That is an example of reporting a measured change, not a general benchmark for agents. The available guidance does not establish a universal pass-rate threshold, sample size, evaluation harness, or definition of “ship-ready.”
How should testing continue after release?
Keep a versioned regression set and run it when prompts, models, routing, knowledge, or tools change. Compare versions on the same cases and evaluators so observed differences have a stable reference point. Add reproducible user-reported failures to the suite rather than treating them as isolated incidents.
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Microsoft Learn describes evaluation as iterative, starting during agent design and continuing through deployment and regression detection. The exact testing and monitoring surfaces depend on the agent’s architecture: declarative configuration checks differ from system-correctness checks for custom engines. No single evaluation platform is required by these practices; choose an approach that can inspect the traces and state your workflow needs, replay cases, support repeated trials, and return actionable failures to the test set.
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