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To evaluate an AI agent, assess how it completes a task—not just the text in its final response. A convincing answer can hide a wrong tool choice, failed handoff, or missed guardrail. These seven common evaluation mistakes can make results misleading or hard to repeat; each has a practical fix.
What should an agent evaluation measure?
An agent evaluation measures a system performing a task: its model behavior, tool selection and calls, handoffs, guardrails, and final result. The right evidence depends on the task. For a simple, directly checkable output, the final result may be enough. For a task involving tools or multiple steps, inspecting the end-to-end trace can reveal failures that the final response conceals. OpenAI describes a trace as the end-to-end record of model calls, tool calls, guardrails, and handoffs for one run (OpenAI: Evaluate agent workflows).
Seven agent evaluation mistakes—and how to fix them
1. Scoring only the final answer
A final answer can look correct even if the agent selected the wrong tool, mishandled a handoff, or violated an instruction along the way. Conversely, a weak final response does not by itself tell you which step failed.
One-line fix: Grade representative end-to-end traces for the decisions and transitions that determine task success.
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Trace grading is useful for questions such as whether the agent chose the right tool or handed off when it should. Use it to find workflow failures, then inspect the relevant trace rather than treating the final score as a diagnosis (OpenAI: Evaluate agent workflows).
2. Starting without representative tasks or a definition of “good”
A score is not meaningful unless the test cases resemble the work the agent is meant to do and the success criteria are clear. Without those, a comparison can reward behavior that is easy to score but irrelevant to the task.
One-line fix: Collect representative task examples and define success criteria before comparing versions.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsOpenAI’s evaluation guidance organizes the work around collecting a dataset, defining metrics, and running comparisons. The practical order matters: decide what success means before interpreting a score (OpenAI: Evaluation best practices).
3. Treating an LLM grader as ground truth
A model grader can assess nuanced responses, but it can also misjudge them. Ambiguous tasks, grader defects, and problems in the evaluation harness can all make a sound agent appear to fail—or a weak one appear to pass.
One-line fix: Use deterministic checks where possible, then investigate grader disagreements and the task and harness setup.
If a result is directly verifiable, such as whether a required field is present or a tool returned a specified value, a deterministic check is generally easier to audit. Use a model grader when judgment is genuinely flexible, and examine cases where its assessment conflicts with other evidence. Anthropic discusses grader, harness, and task-ambiguity failures in its guide to agent evaluations (Anthropic: Demystifying evals for AI agents).
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Some questions are easier to answer by comparing two outputs, classifying a result, or scoring it against explicit criteria than by asking a grader to assign a broad, open-ended quality score. A less ambiguous judgment is easier to interpret.
One-line fix: Turn the target behavior into a comparison, classification, or explicit rubric when the task allows it.
OpenAI’s guidance says, “LLMs are better at discriminating between options,” and recommends comparison, classification, or criterion-based scoring for suitable evaluations. This is guidance for choosing a grading format, not a quantified guarantee that one format will always be more accurate (OpenAI: Evaluation best practices).
5. Running an ad hoc suite that cannot be repeated
Inspecting a single trace can help debug one failure, but a handful of one-off checks cannot reliably show whether a prompt, model, or workflow change improved performance across tasks.
One-line fix: Once success criteria are clear, turn representative tasks into a dataset and rerun the evaluation after changes.
Trace inspection is suited to debugging individual runs; a dataset-based evaluation gives you a repeatable basis for comparisons and benchmarking. The two serve different purposes and work best together (OpenAI: Evaluate agent workflows; OpenAI: Evaluation best practices).
6. Ignoring variability across runs
One run can conceal nondeterminism: the same case may produce different outcomes on separate attempts. If that variation matters to your application, a single result is not enough to characterize behavior.
One-line fix: Repeat cases where variability matters and keep monitoring for failures as the application changes.
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7. Assuming an evaluation platform is still available
Evaluation tools and APIs change, so old instructions can become unreliable. For example, OpenAI’s evaluation-best-practices page, checked October 7, 2026, stated that its Evals platform would become read-only on October 31, 2026, and was scheduled to shut down on November 30, 2026. Those are dated lifecycle details, not timeless guidance; the official notice should be checked for current status before relying on it (OpenAI: Evaluation best practices).
One-line fix: Check the official lifecycle notice before adopting or documenting a platform-dependent workflow.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose the right evaluation approach
Match the method to the question you need answered. These approaches are complementary: use traces to understand how an agent behaved, grading to judge whether it met the task criteria, and repeatable runs to compare changes or detect variation.
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Quick Recap
| Evaluation approach | What it helps answer | Best fit |
|---|---|---|
| Final-response grading | Did the response meet the task’s output requirements? | Tasks where the final result is the main outcome and intermediate steps are not material. |
| Trace grading | Did the agent make the right decisions and transitions, including tool calls and handoffs? | Multi-step or tool-using tasks where the path to the result matters. |
| Deterministic checks | Did a directly verifiable condition pass? | Requirements that can be checked without subjective judgment. |
| LLM grading against criteria | How well did a response meet flexible or nuanced criteria? | Cases where a deterministic rule cannot capture the judgment needed; audit disagreements and task setup. |
| Dataset-based repeated evaluation | Did behavior change across representative tasks or repeated runs? | Regression checks, comparisons between versions, and cases where variability matters. |
A practical evaluation workflow
- Choose representative tasks. Build a dataset from the work the agent is expected to perform.
- Define success before scoring. Write criteria that reflect task outcomes, including relevant tool use, handoffs, and guardrails.
- Use the clearest suitable grader. Prefer a deterministic check for directly verifiable outcomes; use comparison, classification, or rubric-based model grading when those formats fit better.
- Inspect traces for workflow questions. Review model and tool calls, guardrails, and handoffs when you need to diagnose how an outcome occurred.
- Repeat runs where results may vary. Use multiple attempts for cases where nondeterminism affects the decision you need to make.
- Rerun after changes. Keep evaluation connected to changes in prompts, models, and workflows, and monitor for new failures.
- Verify tool lifecycle details. Check current official documentation before relying on a platform’s availability or API behavior.
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