A passing check proves that an agent satisfied the particular test that ran—not that it understood the request, used its tools correctly, or left the real system in the right state. To judge whether an agent made the right decision, inspect what it interpreted, what it did, what evidence it relied on, and what actually changed.
What a passing check does—and does not—prove
An evaluation result is bounded by its task, grader, agent configuration, harness, environment, and resource budget. If a test checks only whether the agent returned a required phrase, it cannot establish that a reservation was made, a file was changed correctly, or an action was authorized. OpenAI’s shared playbook for trustworthy third-party evaluations, published May 29, 2026, recommends describing these conditions and the validity checks behind a result.
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That is not the same as saying every test is defective. A result can be meaningful evidence for the specific tasks and conditions tested. It becomes misleading when presented as a broader guarantee about production reliability or safety.
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Check the outcome, not just the agent’s claim
A transcript records interactions; an outcome is the final state of the environment. Anthropic’s guide to evaluating AI agents, published January 9, 2026, makes this distinction with a booking agent: saying that a flight is booked is not proof that a reservation exists.
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For any task that acts on an external system, verify the resulting state independently. Did the booking appear? Was the intended record updated? Did the agent send the right message to the right recipient? A success message is useful evidence of what the agent reported, but not a substitute for checking the effect.
Find where the decision went wrong
A wrong outcome may begin well before the final response. The agent can misunderstand intent, choose an unsuitable tool, pass invalid arguments, misread a tool’s response, invent unsupported information, or stray from its plan. Later steps can conceal the first consequential mistake, so review the full execution trace rather than only the last action.
Microsoft Research’s AgentRx framework article, published March 12, 2026, groups failures into nine categories: plan-adherence failure, invented information, invalid tool invocation, tool-output misinterpretation, intent–plan misalignment, underspecified intent, unsupported intent, triggered guardrails, and system failure. Its taxonomy helps separate a bad decision from the step that caused it.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsThe article analyzes 115 manually annotated failed trajectories from τ-bench, Flash, and Magentic-One. That is a benchmark dataset, not an estimate of how often agents fail in production. The authors report that AgentRx improved failure-localization accuracy by 23.6% and root-cause attribution by 22.9% against prompting baselines in their experiments; those figures describe the authors’ comparisons, not a general guarantee.
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Evaluate the agent in the conditions that matter
A test harness—the setup that runs tasks, supplies tools and environment state, and records results—can shape an agent’s behavior. Simplified tools, generous retries, a different budget, or easier recovery can make the tested system unlike the deployed one. Conversely, a result from a capable setup supports claims only within the tasks and conditions it covers.
For a useful evaluation, make the task clear and demonstrably solvable, use a production-like isolated environment, and choose graders suited to the judgment being made. Deterministic checks work well for objective conditions; model-based graders can assess flexible responses; human review or calibration is appropriate when judgment quality matters. Rigidly requiring one exact action sequence can reject valid alternatives, so enforce a sequence only when that sequence itself is required.
Include both cases where the agent should act and cases where it should refrain. Use reference solutions to detect flawed tasks or graders, and check for validity hazards such as reward hacking, contamination, invalid tasks, refusal effects, and evaluation awareness. Anthropic suggests that 20–50 simple tasks drawn from real failures can be a useful starting set—not a universal sample-size guarantee. Turn production incidents and support reports into regression cases, then rerun evaluations after changes.
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Agents may behave differently on repeated runs. Anthropic distinguishes two metrics: pass@k is the chance of at least one success in k attempts, while pass^k is the chance that every one of k attempts succeeds. The first suits a workflow that can retry until it finds a usable answer; the second is more relevant when every run must succeed.
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For illustration, if each trial has a 75% success rate and the three trials are independent, the chance all three succeed is about 42%. That is a mathematical example under those assumptions, not a measured general agent reliability rate. Anthropic also cautions that success varies by task: a system can perform well on one task and poorly on another, and a task that passes one evaluation run may fail the next.
Report the metric that matches the product requirement and show results at task level where possible. A single successful run establishes that the agent can succeed at least once under those conditions; it does not show how consistently it will do so.
Make consequential actions verifiable and bounded
Before an agent changes an external system, define what it is allowed to do, what requires approval, and what counts as a correct final state. During review, check tool choices and arguments, intermediate evidence, policy constraints, and the resulting state—not merely whether the task was marked complete.
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A credible report should identify whether it measures capability, safeguard performance, or a controlled comparison; name the system and tools; describe the prompt, harness, environment, safeguards, retries, and budget; and explain the validity checks. For comparisons, hold tasks, scoring, harness, and budgets fixed. OpenAI’s evaluation playbook also emphasizes disclosing the setup used to elicit the strongest credible capability claim.
The practical question is not simply, “Did the agent pass?” It is: did it interpret the request correctly, act within its authority, use trustworthy evidence, and leave the environment in the intended state—and does it do that consistently under the conditions where people will rely on it?
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