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An AI agent can give the correct answer and still fail at the job you assigned it. When a task requires browsing, using software, or changing something in an external system, a convincing final response is not proof that the requested outcome happened. Evaluate the result, the agent’s actions, and—when possible—the state of the system it was supposed to change.
Why a correct answer is not proof of success
A language model’s final response shows what it said. It does not, by itself, show what it did. An agent might correctly explain how to update a record without updating it, report that a file was saved when no file exists, or find a plausible answer while missing required evidence.
For an informational question, the answer may be the deliverable. For an agent task, the deliverable is often an end state: a message sent, a setting changed, a report created, or a question answered using specified sources. The evaluation should match that goal. Snowflake recommends looking beyond the final response to outcomes, tool use, intermediate decisions, and policy compliance; NVIDIA distinguishes tool-call measures from full task completion (Snowflake’s guide to evaluating AI agents; NVIDIA’s guide to evaluating agents from tool calls to task completion).
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Tool-assisted work has several stages, and success at one stage does not guarantee success at the next.
- Tool selection: The agent may choose an irrelevant tool or skip a tool the task requires.
- Arguments: It may call the right tool with missing, invalid, or incorrect inputs.
- Interpretation: It may misread an error, a partial result, or a tool’s response.
- Workflow completion: A valid call may be only one step in a larger job. The agent might still need to verify, save, submit, or communicate the result.
That is why call accuracy and task success answer different questions. A syntactically valid call can leave a required update or check undone. Anthropic’s description of agent evaluations likewise treats the agent, its tools, and its environment as parts of an evaluation loop, rather than treating the final text as the whole task (Anthropic’s overview of evaluations for AI agents).
What to inspect when evaluating an agent
Write down the requested end state and any constraints before scoring a run. Then keep these dimensions separate so a polished response cannot conceal a failed action.
| Dimension | Question to ask | Useful evidence |
|---|---|---|
| Outcome | Did the agent meet the user’s goal? | The requested deliverable or a task-specific test. |
| Execution | Did it choose appropriate tools, use valid arguments, and respond correctly to results? | The tool trace, including calls, inputs, outputs, and errors. |
| State | Does the environment or external system show the required result? | A read-back, inspection, or other check of the resulting state. |
| Process and policy | Did it follow required steps and avoid prohibited actions? | The trace and checks against the task’s constraints. |
| Repeatability | Does it work across repeated runs and reasonable task variations? | Results across runs and variations, not one successful attempt. |
This is an evaluation frame, not a universal scoring formula. The cited guidance supports examining these distinct dimensions, but it does not establish one score or pass threshold that fits every agent and task.
Verify changes in the system, not just in the response
For actions that change software or external state, check the result where it matters. If an agent says it created a calendar event, inspect the calendar. If it claims a setting was changed, read the setting back. If it reports a code change, run a task-appropriate test or inspect the resulting files. NVIDIA’s evaluation guidance emphasizes execution environments that track state and allow the world to be inspected after tool use (NVIDIA’s evaluation guidance).
A trace helps explain how the agent reached its claim, but a trace and a state check answer different questions: the trace shows attempted execution; the state check shows whether the required result is present. Neither should be replaced by a confident status message.
Match the grading method to the task
Some tasks have objective results: a required file exists, a test passes, or a record contains the requested value. Use direct checks where they are available. Other tasks allow several acceptable outcomes, so a rubric may be more appropriate than exact string matching. For browsing and research tasks, inspect whether the agent gathered and connected the evidence needed to support its answer—not just whether the answer sounds plausible.
Anthropic describes evaluations involving tools, an environment, and an agent loop. OpenAI’s system card describes task-specific tests and rubric-based decomposition for objective task evaluation (Anthropic’s evaluation overview; OpenAI’s Model Spec introduction). BrowseComp, a benchmark for difficult questions requiring browsing and multi-hop retrieval, is another example of why research-agent evaluation must consider the work of finding evidence, not only the fluency of the answer (OpenAI’s BrowseComp overview).
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One successful run shows that an agent can complete a task under those conditions. It does not establish that it will do so consistently, handle reasonable variations, or stay within safety constraints. Repeat tasks and vary details that should not change the expected result; track consistency, predictability, robustness, and safety alongside accuracy. The GAIA reliability dashboard presents these as separate dimensions for agent reliability (GAIA reliability dashboard).
Keep results broken out by dimension. An agent that usually selects the right tool but often fails to verify a change needs a different remedy from one that reliably executes actions but gives inaccurate explanations. A single aggregate pass rate can hide that distinction.
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