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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsUse both, but assign them different jobs: human reviewers should define and audit the quality standard, while an LLM judge can grade repeatable cases at scale after you have measured how well it agrees with people. For an AI agent, evaluate its decisions and actions—not only its final answer. Neither method is universally better; the right mix depends on the task, the consequences of errors, and how reliably an automated judge handles your rubric.
What each evaluation method is good for
Human review and LLM-as-a-Judge solve overlapping but different problems. People can interpret context, spot unexpected failure modes, and establish labels for cases where the right answer is not straightforward. That judgment takes time and money, however, and experts may disagree.
An LLM judge can apply a rubric repeatedly and at scale, often with less turnaround time than a human review process. But a model’s score is not ground truth: performance varies by task, and judges can be influenced by answer order or verbosity. OpenAI’s evaluation guidance recommends comparing automated judgments with human labels before scaling them. Its central warning is that no strategy is perfect: LLM-as-a-Judge quality depends on context, while expert human annotation is expensive and time-consuming.
Compare the methods on your task
| Decision factor | Human review | LLM judge |
|---|---|---|
| Best fit | Establishing labels, interpreting ambiguous cases, and reviewing consequential outcomes. | Applying a suitable, repeatable rubric to larger volumes after validation. |
| Agreement and reliability | Reviewers can disagree; use a clear scorecard, multiple review rounds, and an aggregation approach such as consensus votes. | Measure agreement with human labels and inspect disagreements; confidence in a score does not establish its accuracy. |
| Cost and turnaround | Expert review takes time and money. | Generally easier to scale, though its quality depends on task context. |
| Bias and robustness | People can interpret context but may not apply criteria consistently. | Can show position and verbosity bias; test whether changing answer order or length changes the result. |
| Agent behavior covered | Can inspect the trajectory, actions, and outcome together. | Can assess these too only if the evaluation input and rubric include them—not merely the final response. |
These are decision factors, not a universal scoring formula. OpenAI’s guidance suggests that pairwise comparisons or pass/fail checks may be more reliable than unconstrained open-ended scoring for some tasks. Choose the grading format that matches a criterion you can define and validate.
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How to build a calibrated evaluation workflow
- Define success. State what the agent should accomplish and what counts as failure. Build a representative set of ordinary cases, edge cases, and adversarial cases where appropriate.
- Write a rubric people can apply. Define the criteria, give examples of score levels, and set a pass/fail threshold if the decision requires one. Have human reviewers refine the scorecard over multiple rounds.
- Create a human-labeled calibration set. Ask reviewers to grade a sample of cases using the rubric. Where reviewers disagree, examine whether the rubric is ambiguous or the task genuinely calls for judgment; consensus votes are one simple way to aggregate human assessments.
- Test the LLM judge against those labels. Measure agreement, then read the disagreements rather than relying on a single aggregate score. Check whether answer order, response length, or task context shifts the judgment.
- Automate only the suitable checks. Use the judge for criteria it applies consistently. Keep ambiguous, high-impact, or poorly specified cases in human review, and audit automated judgments for failures.
- Revisit the evaluation as the system changes. Changes to the agent, its tools, or its operating environment can change the failure modes. Continue sampling and checking judge agreement rather than treating initial validation as permanent.
Evaluate the agent’s trajectory, not just its answer
A polished final response can conceal a wrong tool call, a bad intermediate decision, or a handoff failure. Include the actions and context needed to assess how the agent reached its result. OpenAI’s evaluation guidance identifies instruction following, functional correctness, tool choice, argument precision, and agent handoffs as relevant dimensions.
- Final result: Did it follow the instruction and produce a functionally correct outcome?
- Tool selection: Did it choose the appropriate tool for the task?
- Tool arguments: Were the arguments precise and valid for the intended action?
- Handoffs: In a multi-agent system, was work passed to the right agent at the right boundary?
- Trajectory errors: Where did the process go wrong, even if the final answer appears acceptable?
- Judge sensitivity: Does the score change when answer order, length, or relevant task context changes?
For an example focused on agent trajectories, Counsel: A Meta-Evaluation Dataset for Agentic Tasks tests whether critiques of customer-support and coding agent trajectories are valid by comparing them with human meta-evaluations. The authors report 1.13k human meta-annotations across 225 trajectories and a Krippendorff’s alpha of 0.78 between human meta-annotators. That alpha describes human agreement on this dataset; it is not an LLM judge accuracy score.
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Why a longer judge prompt is not a guarantee
Detailed instructions can make a rubric easier to follow, but they do not by themselves prove that a judge is reliable. The AAAI 2025 paper “Evaluating the Evaluator: Measuring LLMs’ Adherence to Task Evaluation Instructions” reports limited overall benefit from more detailed judge prompts in its experiments; it also found that perplexity sometimes aligned better with human judgments on textual quality. Those findings are scoped to that paper’s experiments. They do not show that perplexity replaces human review or rubric-based evaluation for agent tasks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What benchmark results can—and cannot—tell you
OpenAI’s PaperBench uses a rubric-based benchmark for research agents, with an LLM judge and a separate benchmark for judges. It covers 20 ICML 2024 papers and 8,316 individually gradable rubric tasks. OpenAI reported a 21.0% average replication score for the best-performing tested setup, Claude 3.5 Sonnet (New) with open-source scaffolding. That is a result for that benchmark run and configuration—not a general estimate of agent capability or proof that an LLM judge will work equally well for another task.
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