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Build continuous scoring for an agent by defining observable success criteria, keeping a versioned set of representative tasks, grading both outcomes and execution traces, and running comparable evaluations whenever relevant behavior changes. Then monitor real runs, review failures, and add useful cases to the offline set. The score is only as trustworthy as the task definition and graders behind it: a convincing final answer does not prove that an agent completed the requested action.
What continuous scoring should tell you
A continuous evaluation pipeline answers two practical questions: did the agent accomplish the task, and did it do so in an acceptable way? A single quality score can hide the difference between a correct result reached through a risky action and a safe interaction that never completed the task. Keep the underlying dimensions visible, even if you also calculate an aggregate score.
For an agent, success often depends on the state of an external environment, not just the text in its final response. A booking agent saying it made a reservation is not evidence that a reservation exists. Define success in terms of observable outcomes, such as a confirmed record in the relevant system, and separately evaluate process requirements such as correct tool choice, policy compliance, and an appropriate handoff. Anthropic explains this distinction between task outcome and transcript in its guide to agent evaluations.
Continuous scoring is not merely scheduling the same benchmark. It is a feedback loop: curate tasks, run and inspect evaluations, compare versions, monitor deployed behavior, and turn newly discovered failures into future tests. OpenAI’s evaluation best practices recommend defining the objective first, collecting data, choosing metrics, comparing runs, and evaluating continuously as the application changes.
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Define the score before choosing the metric
Write success as observable criteria
For each task type, state what must be true for a successful run. Include the final state and any process conditions that matter. For example, a support agent task might require an accurate answer grounded in the supplied account details, no disclosure of restricted information, and a handoff when the request exceeds the agent’s authority. A tool-using workflow might additionally require a specified record to be created or updated.
Separate criteria that answer different questions. “Did the requested change happen?” is an outcome check. “Did the agent call the correct tool?” is a process check. “Was the explanation clear?” is a communication-quality judgment. Combining them prematurely makes a failure harder to diagnose and can allow a strong score on a secondary dimension to mask a critical failure.
- Outcome: Did the task reach the correct state in the target system?
- Process: Were required actions, tool choices, handoffs, and policy checks handled correctly?
- Response: Was the final message accurate and appropriate for the user?
- Risk: Did the run take a prohibited, unsafe, or unauthorized action?
Keep metrics tied to decisions
Each metric should help someone decide what to fix or whether a change is acceptable. A pass/fail assertion can be right for a mandatory business rule; a rubric score can help assess a semantic quality; a pairwise judgment can help decide which of two versions is better. Avoid a vague “agent quality” label without an explicit rubric. OpenAI’s best-practices guidance favors evaluation tasks framed as comparison, classification, or scoring against defined criteria rather than open-ended generation.
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Decide which failures are critical before using an aggregate. If an aggregate is useful for trend tracking, retain the component scores and document the weights and thresholds. For example, a team could make policy violations a hard failure while separately tracking task completion and response quality; the exact rule depends on the application and is not universal. Do not choose a release threshold merely because it produces a convenient pass rate.
Build and version a representative dataset
Include ordinary work and hard cases
A useful dataset represents the tasks the agent is expected to handle, not only clean demonstrations. Start with representative inputs and expected outcomes. Add expert-authored examples where domain knowledge is needed, historical cases, suitable user feedback, and known difficult or adversarial inputs. OpenAI’s dataset guidance describes datasets as evolving collections and recommends expert annotations when subject-matter expertise is required.
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For an agent, a row should capture enough to judge the run, not just a reference answer. Depending on the environment, that may include the request, relevant context, expected end state, allowed or required actions, and annotations used by graders. There is no single schema that fits every agent. The key is that the expected outcome and any important action constraints are explicit enough to evaluate consistently.
Grow the set without losing comparability
When a production review reveals a new failure mode, add a de-identified and policy-appropriate case to the evaluation set when it is suitable for reuse. Record why it was added and what it tests. Keep dataset versions so a score change can be interpreted: it may result from a new agent version, a changed dataset, or an updated grading rule. Where possible, preserve a stable core set for trend comparisons while expanding a broader set to cover newly found cases.
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Do not silently rewrite expected outcomes to match the latest agent behavior. Update an expected result when the task definition or business rule has genuinely changed, and retain enough history to explain that change. A dataset should represent the intended product contract, not enshrine accidental behavior from a prior model.
Choose graders that fit the criteria
| Grader approach | Best fit | What to inspect |
|---|---|---|
| Deterministic code check | Exact structure, required fields, identifiers, prohibited actions, or a known expected tool call | Whether the assertion measures a real requirement and handles valid variations |
| Rubric-based model judge | Semantic correctness, relevance, clarity, or other criteria that are difficult to express as a simple assertion | The rubric, evidence available to the judge, and agreement with expert review |
| Pairwise comparison | Choosing between two versions when relative preference is easier to judge than an absolute score | Which criteria determine preference and whether comparisons are consistent |
These approaches can be combined. Use code for facts that have a clear machine-checkable rule: schema validity, a required field, a matching identifier, or a prohibited operation. Use a model-based rubric for semantic criteria where a rigid check would reject acceptable wording or miss meaning. LangChain’s overview of evaluation types describes code evaluators, LLM judges, and composite evaluation; Anthropic also discusses binary, weighted, and hybrid scoring.
Keep each grader’s result inspectable. A pipeline that reports only one blended number makes it difficult to tell whether a release failed because of task completion, tool selection, or response quality. If you combine dimensions, document the combination rule and preserve the unaggregated values. For high-impact rules, make the release decision explicit about whether a failure is disqualifying or merely a tracked quality signal.
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Make model judges auditable
A model judge is another model-based component, not ground truth. Give it a bounded rubric, the task criteria, and the evidence needed to make its judgment. Ask for criterion-level outcomes and a brief evidence-based rationale rather than an unconstrained essay. Compare judge outcomes with human or subject-matter expert annotations, particularly on subjective or high-stakes criteria. OpenAI’s dataset documentation describes annotations and expert input; automated graders enable repeatability at scale but do not replace human ground truth.
Review disagreements rather than averaging them away. If a judge rewards a polished explanation even when the external action failed, revise the rubric or supply the missing state evidence. If reasonable outputs are penalized for phrasing, clarify the criterion so it evaluates meaning rather than a favored wording. Periodically recalibrate after changes to the judge, rubric, model, or task definition.
Capture traces so scores can explain failures
A dataset score can show that performance changed; a trace helps explain how. Preserve the parts of each run needed to reconstruct agent behavior: model calls, tool calls and results, guardrail decisions, handoffs, relevant intermediate results, and final output. Apply appropriate access controls and data-retention rules to traces because they may contain sensitive user or business information.
Trace grading can answer workflow questions that a final-answer grader cannot: did the agent select the right tool, hand off at the right time, violate a policy, or improve end-to-end after a prompt or routing change? OpenAI’s agent evaluation guide treats trace grading and dataset evaluation as complementary: traces support diagnosis, while repeatable datasets support comparisons across changes.
Use representative failed traces to identify whether the remedy belongs in the prompt, tool interface, routing, guardrails, or task definition. Do not assume that a low score names its own cause. Inspect the sequence of decisions and the external outcome, then turn a reproducible failure into a dataset case if it represents a behavior the agent must handle.
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Run comparable evaluations in the development pipeline
Evaluate changes that can affect behavior
Run the relevant suite whenever a change could alter agent behavior: model or prompt updates, tool definitions, routing logic, guardrails, and other workflow changes. Fast deterministic checks can run frequently; broader evaluation suites can be reserved for changes with wider impact. The sources support evaluating changes and comparing repeatable runs, but do not establish one universal CI configuration or release threshold.
For every run, record the application or workflow version, dataset version, grader and rubric version, and results. Include enough configuration context to make a comparison meaningful, such as model and relevant settings where your system exposes them. A score without this context cannot reliably tell a regression from a changed test set or evaluator.
- Define the change: identify which tasks and dimensions it could affect.
- Select the suite: run relevant deterministic checks and the broader dataset evaluation for the affected workflow.
- Compare against a baseline: inspect overall and per-criterion results, as well as important individual failures.
- Review traces: investigate unexpected changes and critical failures before attributing a cause.
- Apply the release policy: use documented, risk-appropriate gates rather than an unexplained aggregate score.
- Store the run: retain versions and results so later changes can be compared fairly.
Baselines are decision aids, not proof of correctness. A stable score can mean the test suite is stable while missing a new failure mode. A changed score can reflect a dataset or grader update rather than a product regression. Label these changes clearly in the evaluation record.
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Offline evaluation checks curated examples under repeatable conditions. It is useful for regression comparisons, unit-like assertions, and backtests against appropriate historical cases. Online evaluation observes live traces and can reveal anomalies, degradation, or edge cases missing from the curated set. LangChain’s evaluation documentation describes offline and online evaluation as parts of a cycle, with production observations informing future test cases.
Monitoring should connect a signal to review. Depending on the application, signals may include changes in outcome checks, policy failures, tool errors, handoff patterns, or user feedback. These are candidate dimensions to monitor, not a universal alert policy. Set alerting and sampling according to the workflow’s risk, volume, and privacy constraints; the sources do not prescribe a single threshold for all agents.
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- Capture and evaluate production traces under appropriate privacy and retention controls.
- Flag anomalous results or user-reported failures for human review.
- Determine whether a case reflects a real product failure, ambiguous task specification, or grader error.
- When appropriate, add a safe, reusable version to the offline dataset with an explicit expected outcome.
- Run the expanded suite on subsequent relevant changes and track the case’s result.
This closes the loop between actual use and development testing. Without that feedback, a benchmark can remain clean while the product encounters situations it was never asked to handle.
Account for variability and grader error
Agent outputs can vary from one run to another, so one successful run is not definitive evidence of stable behavior. For important or unstable tasks, use multiple trials and inspect the variation. Anthropic’s agent evaluation guide discusses repeated trials as a way to obtain more consistent results. Preserve trial-level outcomes where possible rather than reporting only an average that hides intermittent failures.
Variability and grader error are separate problems. Repeating a run can reveal inconsistent agent behavior, but it cannot fix a rubric that rewards the wrong outcome. Validate that the grader corresponds to the stated task objective, inspect disagreements against human judgment, and revisit criteria when an evaluator consistently gives surprising results. A mislabeled expected result or poorly chosen threshold can make a reliably computed score misleading.
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| Approach | Can suit teams that need | Trade-off to assess |
|---|---|---|
| Custom scripts and CI checks | Deterministic assertions and a small, clearly defined evaluation surface | The team may need to maintain dataset handling, grader orchestration, and reporting |
| Evaluation or observability platform | Some combination of traces, datasets, experiments, offline evaluation, online evaluation, or monitoring | Capabilities, integration fit, data handling, and adoption effort vary; verify current details with the provider |
Tool choice should follow the workflow rather than the product category. Anthropic names Braintrust, LangSmith, Langfuse, Arize Phoenix, and Arize AX as examples of available tools, not as a ranking or endorsement. Its article on agent evaluations also notes that a team may combine tools, build its own evaluation framework, or begin with simple scripts. A platform cannot compensate for unclear tasks or misaligned graders.
- Framework and provider integration: can it capture the traces and run the evaluations your existing agent stack needs?
- Offline workflow: can you manage datasets, experiments, and repeatable comparisons?
- Production workflow: does it support the trace capture and online evaluation you actually need?
- Data requirements: do residency, privacy, retention, and self-hosting needs fit?
- Grader transparency: can reviewers inspect individual judgments and customize evaluators?
- Maintenance cost: is platform adoption justified compared with maintaining a smaller custom harness?
Confirm current product capabilities and terms directly in vendor documentation before choosing a tool. The relevant comparison is whether the setup supports your data, evaluation loop, and operational requirements—not whether it advertises an “agent eval” feature.
Plan around the OpenAI Evals lifecycle dates
OpenAI’s dataset guide states that the Evals platform will become read-only for existing users on October 31, 2026, and is scheduled to shut down on November 30, 2026. The same page points to Evals for advanced features such as external-model evaluation, API interaction, or larger-scale runs. These are scheduled product lifecycle dates, so check the official guidance before basing a new implementation or migration plan on that platform.
A practical operating checklist
- Each task has observable success criteria, including external state where relevant.
- Outcome, process, response quality, and critical risk are not hidden inside one opaque score.
- The dataset includes representative normal, difficult, historical, and expert-reviewed cases as appropriate.
- Dataset, agent, grader, and rubric versions are recorded for each run.
- Deterministic checks handle exact rules; rubric judges handle semantic criteria; pairwise comparisons are used where relative preference is the clearer task.
- Individual grader outputs and representative traces can be inspected.
- Relevant behavior changes trigger repeatable evaluation and documented comparison.
- Production anomalies and reviewed failures can become new offline regression cases.
- Trials and human review are used where variability, subjectivity, or risk makes a single automated score insufficient.
OpenAI’s evaluation guidance summarizes the operating principle: “Set up continuous evaluation (CE) to run evals on every change, monitor your app to identify new cases of nondeterminism, and grow the eval set over time.” See its evaluation best-practices documentation for the full context.
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