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What a fair test of an agent’s choice requires
An agent that selects between two options is making a decision, and a decision can be evaluated like any other system output. The difficulty is that an agent’s answer is often fluent and confident whether or not the choice was right. A useful test separates three things: whether the agent picked the option you expected, whether it supplied valid inputs to that option, and whether the action achieved the result you wanted. OpenAI’s evaluation guidance makes a similar split, treating tool selection and argument precision as distinct targets from the correctness of the final response (OpenAI, Evaluation best practices).
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Step 1: Name the decision and both alternatives
A test needs two options that can actually be executed and compared. Examples include tool A versus tool B, model A versus model B, or a single-agent workflow versus a multi-step workflow. Vague choices such as “which approach is better” cannot be scored.
Microsoft’s agent-learning documentation recommends a reusable decision policy only when three conditions hold: the alternatives recur often enough to matter, the choice can affect a meaningful outcome, and that outcome can be observed later (Microsoft, Agentic decision making with measurable feedback, dated August 2026). A one-off question asking for advice does not meet that bar, and a test built around it will not tell you much.
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Step 2: Define success before you look at results
Choose the criteria that matter for the job, and decide which one is primary before any run. Common targets are correctness, task completion, safety or policy adherence, latency, and cost. Keep two questions apart: was the response good, and was the selected action appropriate? An eloquent answer can hide a wrong tool call, and a correct tool call can be followed by a poorly worded summary.
Weighting matters. If you combine latency and correctness into one score, the result depends on weights you chose, so state them explicitly or report the measures side by side.
Step 3: Build test cases that represent real use
Include ordinary requests that reflect what users actually send, plus deliberate edge cases: ambiguous wording, missing information, requests where one option is clearly wrong, and requests where either option would work. Use exactly the same cases for both alternatives. Save the prompt, model configuration, available tools, and environment state with each case so the run can be repeated.
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No published source gives a universal case count that is sufficient for every agent decision. The practical rule is to report how many cases you used, how they were chosen, and what they do not cover. A small set can reveal a clear failure, but it cannot prove the agent will choose correctly on requests you did not test.
Step 4: Record the selection and the independent outcome
For each run, log four things:
- The option the agent selected.
- The arguments it passed, or the handoff it made, if the choice involved a tool or another agent.
- The outcome, measured by a check that does not depend on the agent’s own explanation: a database state, a test assertion, a returned status code, or a reviewer’s verdict.
- The configuration and run identifier, so you can trace a surprising result back to its inputs.
Microsoft’s guidance puts it bluntly: “Advice is not execution evidence.” If the agent says it chose option A and explains why, that explanation is not a measurement. Where the system allows it, update the same decision record when execution feedback arrives, then score the decision against that feedback rather than against the recommendation.
Handling nondeterminism
The same agent can choose differently on repeated runs of the same case. Run each case several times and report the selection rate for each option, not a single pass or fail. How many repetitions are enough depends on how much variation you see and how costly a wrong choice is; there is no fixed number in the cited guidance, so set one before the test and state it in your report.
Step 5: Compare the alternatives and report uncertainty
The comparison method depends on the kind of output you are judging.
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When the outcome is objective, such as whether a booking was created or a file was updated correctly, compare success rates for each option across the same case set. Report the rates alongside the counts behind them. A difference of a few percentage points on a small set is not a reliable signal, so treat close results as ties until more evidence accumulates.
Subjective quality
When the question is which response is better, pairwise evaluation is more practical than absolute scoring. AG2’s pairwise evaluation guide reports a win rate with a confidence interval and judges each pair in both presentation orders to reduce position bias, and it allows ties (AG2, Pairwise evaluation). The confidence interval describes uncertainty within that test set. It does not guarantee the set matches production traffic.
Step 6: Check whether the evaluator is biased toward the choice
An agent should not grade its own choice. The more pressing risk is subtler: a judge model, or a human reviewer who sees the agent’s reasoning, may favor the option the agent already picked. A 2025 AAAI paper by Zhuang and co-authors reports choice-supportive bias in LLM agent evaluations, and it says the bias’s expression varied with prompt construction and context (Zhuang et al., AAAI 2025). The study covers 19 open and closed-source LLMs across up to five scenarios, with 284 well-educated human participants in a comparison group. Those numbers describe that experiment; they do not show that every agent or judge behaves this way.
To reduce the risk, use one or more of these controls:
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- Prefer independent outcome checks over judgments of quality wherever an objective result exists.
- Hide which option the agent chose from the grader, and randomize the order in which responses are shown.
- For subjective judgments, use blinded human review on a sample and compare the human verdicts with the automated ones.
Comparison axes for a two-option test
Use the table below to decide what to record for each alternative. Mark the primary axis before you run anything.
| Axis | What to record | What it catches |
|---|---|---|
| Selection | Which option the agent chose on each run | Choices that differ from the intended policy |
| Argument or handoff accuracy | Whether the inputs passed to the chosen option were valid and complete | Right tool, wrong parameters |
| Outcome correctness | Result checked by an independent test or record | Actions that ran but did not achieve the goal |
| Safety and policy adherence | Whether the action stayed inside stated rules | Permitted-looking choices that violate constraints |
| Latency and cost | Time and resource use per run, measured the same way for both options | A correct choice that is too slow or expensive to use |
| Consistency | Selection rate across repeated runs of the same case | Unstable choices that look fine on one run |
What the evidence does and does not establish
The cited guidance supports a clear method: define alternatives, fix success criteria, run both options on the same cases, record selections and independent outcomes, and report uncertainty. It does not provide a universal benchmark, a required sample size, or proof that any learned decision policy improves results. Microsoft’s material describes implementation guidance for its own project, and AG2’s workflow is one practical method rather than a standard. Treat the results of your own test as evidence about the cases you ran.
OpenAI’s evaluation guidance also notes that architecture decisions, including whether to use a multi-agent design, should be driven by evaluation results rather than preference (OpenAI, Evaluation best practices). The same logic applies to a single choice between two options: the test result, not the agent’s confidence, should decide which option stays in production.
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