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Yes. You can test whether an agent picked the better of two options, but only if you measure three things separately: which option it selected, whether the action it took was valid, and what happened afterward. An explanation that sounds sensible tells you nothing about whether the choice worked. The test has to be set up before the agent runs, on the same cases for both options, with an outcome you can observe without asking the agent to grade itself.
Why a choice needs three separate measurements
When an agent chooses between two tools, two models, or two workflows, a single “was the answer good?” score hides what went wrong. The agent may have picked the right option and supplied bad arguments. It may have picked the wrong option but still produced a plausible final reply. Or the action may have succeeded while the reply to the user was poor. OpenAI’s evaluation best-practices guide separates these targets: tool selection, argument precision, and the correctness of the final response are distinct things to check, and they are scored separately.
The same guide makes a broader point that applies directly to architecture choices. In its words, “The decision to use a multi-agent architecture should be driven by your evals.” In other words, the option you keep should be the one your measured results support, not the one that sounded more capable in a demo.
Set up the test before you run it
Most failed comparisons are decided before any data is collected. Work through the six steps below in order. Each one closes a gap that makes a later result hard to trust.
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1. Name the decision and both alternatives
Write each option as something the agent can execute, not as a general preference. Examples include “tool A, a search API with a fixed schema, versus tool B, a database lookup,” “model A versus model B with the same system prompt,” or “workflow A, which asks a clarifying question first, versus workflow B, which acts immediately.”
Microsoft’s agent-learning project documentation, dated 10 August 2026, describes explicit alternatives and outcome-based feedback for decisions that recur. Its guidance is that a reusable decision policy makes sense only when three conditions hold: the alternatives are stable enough to recur, the choice can affect a meaningful outcome, and that outcome can later be observed. A one-off question such as “which approach should we use?” is not a repeatable decision and cannot be tested in this way. See the Microsoft decision-making documentation.
2. Fix the success criterion in advance
Choose the measures that define a good choice for this job, and decide which one is primary before you look at results. Common options are correctness, task completion, safety or policy adherence, latency, and cost. Keep the answer-quality measure separate from the appropriateness of the selected action. If you later need a single score, write the weighting down first. Otherwise you are only choosing the weights that favour the option you already prefer.
3. Build a fixed case set and run both options on it
Use ordinary requests that reflect real traffic, plus a deliberate set of edge cases: ambiguous inputs, missing data, requests that tempt the agent toward the wrong tool, and inputs that should trigger a refusal or escalation. Run the identical cases through both alternatives. Record the prompt, model version, configuration, tool list, and environment for every run, so that a rerun can reproduce the same conditions. Agent behaviour varies between runs, so a single pass per case is a weak basis for a decision.
4. Record the selection, the arguments, and the outcome
For each case, log which option the agent selected, the arguments it passed or the handoff it made, and an outcome that was checked independently of the agent. The independent check might be a database state, a schema validation, a test that the downstream system accepted the request, or a human verdict on a defined rubric.
Microsoft’s documentation makes the distinction plain: “Advice is not execution evidence.” An agent’s recommendation, or its stated reason for picking an option, is not proof that the option worked. When execution or other feedback arrives later, update the same decision record and score it on that outcome.
5. Compare the two options and report uncertainty
For subjective output quality, a pairwise comparison works well: a judge, human or model, sees two responses to the same task and picks the better one. AG2’s pairwise evaluation guide reports a win rate with a confidence interval, and it judges each pair in both orders to reduce position bias, so that a judge who favours whichever response appears first does not decide the result. Allow for ties rather than forcing a winner on every case.
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For operational decisions, such as whether a tool call succeeded, compare success rates and the other predefined measures across the whole case set, and state the uncertainty that fits the size of the data. A difference of a few cases on a small set may not be meaningful, and the report should say so.
6. Check whether the grader is biased toward the option it chose
Do not let an agent that chose an option grade that choice. A 2025 paper in the Association for the Advancement of Artificial Intelligence (AAAI) proceedings, available as a PDF from AAAI’s journal system, reports choice-supportive bias in LLM agent evaluations: evaluators tended to justify options that had already been selected. The paper says the strength of this effect varied with prompt construction and context. For subjective judgments, use a rubric written before the run, independent outcome checks wherever they exist, and blinded human review for the cases that remain.
Comparison axes for a two-option test
The table below lists the axes that matter for this kind of decision. Record each one per case so that you can see where the two options diverge, instead of reading one blended number.
| Axis | What to record | How to check it |
|---|---|---|
| Option selection | Which alternative the agent chose on each case | Compare against the expected option defined for the case |
| Argument or handoff accuracy | Parameters passed to the tool, or the agent or workflow it handed off to | Schema validation and comparison with the expected values |
| Outcome correctness | Whether the action achieved its intended result | An independent check such as a system state, a test assertion, or a human verdict |
| Response correctness | Whether the final reply to the user is accurate and complete | A written rubric, with pairwise comparison for subjective quality |
| Safety and policy adherence | Whether the chosen action stayed within defined limits | Predefined policy checks on the edge-case set |
| Latency and cost | Time and resource use per completed case | Measured from logs for both options under the same conditions |
State which axis is primary before running the test. Do not collapse unlike results into one score unless the weighting is written down.
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None of the sources reviewed sets a universal number of cases that is sufficient for every agent decision. OpenAI’s guide provides evaluation examples rather than a benchmark protocol, and AG2’s guide explains how to report a pairwise comparison, not how many cases to use. The number you need depends on how much variation the decision has and how large a difference you need to detect.
The AAAI study gives a sense of scale without setting a rule. It evaluated 19 open and closed-source LLM models across up to five scenarios, and it used 284 human participants described as well-educated in its comparison study. Those figures describe that experiment only. They do not show that a test of similar size will be enough for your agent, and the participant group should not be treated as representative of all users.
What the evidence does and does not establish
- Testing a choice is possible when the alternatives and the outcome can be observed. Without a follow-up measurement, a stated preference shows only what the agent said.
- The OpenAI guidance supports separating selection, arguments, and final responses, but it does not supply a fixed evaluation protocol for every agent.
- Microsoft’s material is implementation guidance for its own agent-learning project. It does not prove that any particular learned decision policy improves outcomes.
- AG2’s confidence intervals describe uncertainty in the reported comparison. They do not guarantee that your test set represents production traffic.
- The AAAI findings describe a measured risk under the paper’s experimental conditions. They do not show that every agent or every judge is biased in every task.
In practice, the safest claim you can make after a well-run test is narrow: on this case set, with these criteria and this measured outcome, one option did better than the other, with the stated uncertainty. Extending that result to future requests is a separate judgment that needs its own evidence.
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