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A passing check shows that an AI agent met the grader’s criteria in the tested run. It does not prove that the agent understood the request, used the right tools, respected its authority, or left the real environment in the intended state. To judge whether an agent made the right decision, inspect its reasoning trace, the conditions under which it was tested, and the outcome—not just its success message or benchmark score.
What a passing check actually proves
An evaluation result is bounded by the task, grader, agent configuration, harness, environment, and resource budget that produced it. If a test checks whether an answer contains a confirmation phrase, for example, it may pass an agent that says an action succeeded even when nothing changed. A strong evaluation report makes those boundaries visible rather than treating a score as a general reliability guarantee.
OpenAI’s shared playbook for trustworthy third-party evaluations, published May 29, 2026, recommends describing the system, tools, harness, environment, budgets, elicitation method, and validity checks. A result from a simplified harness does not establish how the agent behaves in deployment; a favorable result in a production-like setup still only supports claims about the tested tasks and conditions.
Did the agent leave the system in the right state?
Separate what the agent did, what it reported, and what actually changed. Anthropic distinguishes a transcript—the record of interactions—from an outcome: the final state of the environment. In its January 9, 2026 guide to agent evaluations, a booking agent’s claim that a flight was reserved is not proof that a reservation exists. Verify consequential changes against the system of record, such as the booking, account, document, or ticket itself.
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That distinction matters whenever an agent can act on external systems. A response can be plausible and confident while the operation failed, affected the wrong item, or succeeded only partly. Define the intended final state in observable terms, then check it directly after the agent acts.
Where a wrong decision enters the trajectory
A final failure can be the downstream effect of an earlier breach. Microsoft Research’s AgentRx framework article, published March 12, 2026, groups agent failures into nine categories: plan-adherence failure, invented information, invalid tool invocation, misinterpretation of tool output, intent–plan misalignment, underspecified intent, unsupported intent, triggered guardrails, and system failure.
In practice, inspect the first point where the agent’s behavior stopped matching the user’s goal or the allowed policy. Did it infer a constraint that was never given? Choose a tool that could not complete the task? Misread a returned value? Continue after a failed operation? An error early in a long workflow can be hidden by later steps that look orderly, so the final answer alone may not reveal the cause.
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What to inspect in a trace
- Intent: Did the agent preserve the user’s actual constraints, including what it was not asked or authorized to do?
- Evidence: Were claims grounded in tool results or supplied context, rather than invented or misread information?
- Tool use: Were the selected tool and its arguments valid, necessary, and within policy?
- Execution: Did the agent follow its plan, handle errors, and avoid unplanned actions?
- Outcome: Does the external environment show the requested result?
Microsoft Research based AgentRx on 115 manually annotated failed trajectories from τ-bench, Flash, and Magentic-One. In the authors’ experiments, it reported a 23.6% absolute improvement in failure-localization accuracy and a 22.9% improvement in root-cause attribution over prompting baselines. These figures describe framework results on that analysis task; they are not estimates of production failure rates or proof that a particular agent will behave reliably.
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One successful run is not consistent behavior
Agent behavior can vary from run to run. Anthropic defines pass@k as the chance of getting at least one correct solution in k attempts, and pass^k as the chance that all k attempts succeed. The first is useful when repeated attempts and selecting a successful result are acceptable; the second is more relevant when each run must succeed.
For illustration, if each trial has a 75% success rate and trials are independent, the chance that all three succeed is about 42%. That is a mathematical example under those assumptions, not an observed general reliability statistic. Anthropic notes that success rates can differ across tasks and that a task passing in one evaluation run may fail in the next.
Choose the metric to match the product requirement. If a system can retry safely and a human or downstream process selects a valid result, at-least-one success may matter. If every execution can create a real-world consequence, consistent success across runs is the more relevant question. Repeat stochastic tasks and report task-level results rather than relying only on an aggregate score.
How to build an evaluation that catches the wrong decision
- Define success as the user’s goal and observable final state. Specify what must be true after completion, not merely what the agent should say.
- Record the exact tested setup. Identify the model, prompt, tools, harness, environment, safeguards, retries, and resource budget so readers can interpret the result.
- Evaluate the trajectory and outcome. Retain tool choices and arguments, intermediate evidence, policy-relevant decisions, and checks of external state.
- Use both positive and negative cases. Test when the agent should act and when it should decline, ask for clarification, or stop. This helps expose unsupported intent and failures to respect constraints.
- Make tasks clear and solvable. Use reference solutions to identify defective tasks or graders before interpreting a score as an agent failure or success.
- Match the grader to the claim. Use deterministic checks for objectively verifiable requirements, model-based grading for flexible judgments, and human calibration or review where judgment quality matters.
- Repeat stochastic tasks and report the right metric. Distinguish the chance of at least one success from the chance that every trial succeeds.
- Turn production failures into regression cases. Add failures and support reports to the test set, then rerun evaluations after changes.
- Set authority and approval rules before execution. For consequential actions, specify constraints and required approvals, then verify the resulting state.
Anthropic suggests 20–50 simple tasks drawn from real failures as a useful starting set, not a universal sample-size guarantee. Grading should focus on outcomes and policy-relevant behavior: rigidly requiring one exact action sequence can reject a valid alternative unless the sequence itself is required.
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When a score supports a comparison—and when it does not
For a controlled comparison between systems, keep the tasks, scoring, harness, and budgets fixed. If the aim is to show the strongest credible capability rather than a controlled comparison, disclose the elicitation setup and interpret the result accordingly. In either case, explain whether the evaluation measures capability, safeguard performance, or comparative performance, and state the system, tools, budget, harness, and validity checks.
Check for hazards that can make a result misleading: reward hacking, contaminated test data, invalid or unsolvable tasks, refusal effects, and evaluation awareness. These checks do not guarantee production reliability; they help establish what the evaluation can reasonably support.
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