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Build continuous scoring as a feedback loop: define what successful work looks like, test a versioned set of representative agent tasks with graders suited to each criterion, preserve traces so failures can be diagnosed, and run relevant evaluations whenever behavior may change. After deployment, monitor real runs and turn reviewed failures into new regression cases. A score is useful only when it measures the outcome you care about—not merely what the agent says it did.
What continuous scoring should tell you
An agent evaluation pipeline should answer two different questions: did the agent accomplish the task, and did it follow an acceptable process while doing so? A final response alone may answer neither. A booking agent can claim that a reservation was made without creating one; a workflow can reach the right end state through an unsafe action. The evaluation therefore needs observable success criteria tied to the task and, where relevant, checks on the actions that produced the result. Anthropic’s guide to agent evaluations distinguishes an environment outcome from the transcript that describes it.
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Continuous scoring is not one metric or one nightly benchmark. It is a repeatable operating loop that connects development checks with post-deployment learning. A typical loop contains a task definition, a growing dataset, a set of graders, trace capture, comparable evaluation runs, and a review path from production issues back into the dataset. The parts work together: dataset scores reveal that something changed, traces help explain where, and reviewed real-world cases make later runs more representative.
Keep outcome and process dimensions visible separately. If a weighted total is useful for comparing runs, do not let a high score on a soft quality dimension conceal a critical policy violation or failed end state. A score should make trade-offs legible rather than bury them.
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Define success before choosing a score
Start with the user task and state what success looks like in observable terms. Specify the input and context the agent receives, the end state that must be true, and any intermediate behavior that matters. For one task, the central check may be whether the answer is factually correct; for another, whether a required tool action occurred, the correct specialist received a handoff, or a prohibited action was avoided. OpenAI’s evaluation best practices put defining the objective ahead of selecting metrics or running comparisons.
Separate criteria that need different kinds of evidence. For example, an agent handling a refund might need to identify an eligible order, follow the permitted refund policy, call the correct operation, and leave the account in the expected state. A fluent explanation is not evidence that the account changed. Conversely, a correct end state does not necessarily prove that a restricted action was handled appropriately.
- Outcome criteria: Is the task’s expected end state true? Is the returned answer correct, complete, or useful for the stated request?
- Process criteria: Did the agent choose an allowed tool, provide required inputs, respect policy, and hand off when appropriate?
- Communication criteria: Did the agent accurately represent what it completed, what it could not complete, and any uncertainty that matters to the user?
Write a criterion so a reviewer can decide what evidence would pass or fail it. Avoid an undefined label such as “good answer” as the sole target: it blends correctness, tone, policy, and task completion into an outcome that is difficult to diagnose. For criteria where failure severity differs, decide whether they are gates, separately reported dimensions, or inputs to a composite score. That is a product and risk decision, not a universal formula.
Build a dataset that reflects the work agents actually do
Begin with representative task inputs and expected outcomes, then expand the collection as domain experts, user feedback, evaluations, and production review reveal gaps. Include ordinary cases as well as hard, ambiguous, and adversarial cases that expose meaningful failure modes. OpenAI’s dataset guidance describes datasets as evolving collections and recommends expert annotations when subject-matter knowledge is needed.
For an agent, a useful case usually needs more than a reference response. Capture the request and relevant context, the expected outcome or state, any required or disallowed actions, and annotations that graders need. The exact representation depends on the agent and its environment; there is no single prescribed schema. The point is to preserve enough information to evaluate the criterion rather than infer success from a polished transcript.
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| Case information | Why it belongs in the evaluation |
|---|---|
| User request and relevant context | Defines what the agent was asked to do and the information it was allowed to use. |
| Expected result or end state | Allows the evaluation to check task completion rather than the agent’s claim of completion. |
| Action expectations, where relevant | Records required, allowed, or prohibited tool behavior, handoffs, and policy constraints. |
| Annotations and case metadata | Gives graders and reviewers the domain-specific facts and labels needed to judge the run. |
Version the dataset as deliberately as you version the agent. A changed case, expected outcome, or annotation can change a score even when the application has not changed. Keep enough case identity and version information to interpret a comparison later. Add a new case when a reviewed failure reveals a reusable blind spot; do not turn every isolated anomaly into a test without deciding whether it reflects a behavior the agent should handle.
Dataset coverage matters more than raw size. A large set dominated by easy or duplicate inputs can create a reassuring average while leaving a high-impact workflow untested. Group cases by task, risk, or failure mode so a reviewer can see whether a score shift is broad or concentrated. Include historical examples where they remain representative, but account for changes in policies, tools, or user behavior when deciding whether an older expected outcome is still valid.
Match graders to the criteria
Use deterministic code for properties with crisp rules: valid output structure, required fields, an exact identifier, a forbidden operation, or whether an expected tool call occurred. Deterministic checks are efficient and inspectable, but they cannot reliably judge every question of meaning. A model-based rubric judge can assess semantic correctness, relevance, or clarity when a fixed assertion would be inadequate. LangSmith’s overview of evaluation types describes code evaluators, language-model judges, and composite evaluation; Anthropic discusses binary, weighted, and hybrid task scoring.
For a subjective criterion, give the judge a defined rubric rather than an invitation to produce a general impression. State the criterion, what evidence to inspect, and how to distinguish passing behavior from a near miss or failure. Keep outputs inspectable: a numeric score without the reason or evidence behind it is hard to audit when it disagrees with human judgment. Treat a judge’s score as an evaluation result, not as ground truth.
Pairwise comparison is useful when the question is which of two versions performs better and reviewers find that easier than assigning an absolute number to each answer. Keep the comparison focused on explicit criteria; otherwise a judge may favor style, length, or confidence over task success. OpenAI’s best-practices guide recommends comparison, classification, or scoring against explicit criteria rather than open-ended generation for evaluation tasks.
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If an aggregate score is needed, document the component scores, weights, and thresholds. Maintain a separate view of critical failures so a weighted average cannot compensate for a failed safety or business rule. A team may choose a release gate for a particular measure, but the sources do not establish one universal numerical threshold for agent quality. Set gates based on the risk and the evidence available for the workflow.
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A dataset score can show that a change coincided with a regression, but it usually cannot explain the failure by itself. Preserve traces that let a reviewer reconstruct the run: model interactions, tool calls and results, guardrails, handoffs, relevant intermediate results, and the final output. OpenAI’s agent evaluation guide treats trace grading and dataset evaluation as complementary: traces support diagnosis of workflow behavior, while repeatable datasets support comparisons across changes.
Choose trace fields according to what a reviewer needs to establish the task outcome and process. A trace should reveal whether the agent chose the wrong tool, supplied invalid arguments, ignored a tool result, handed off too early or too late, or violated a policy. Redact or restrict sensitive information according to the system’s data-handling requirements; observability is not a reason to collect data without a purpose.
Use trace review to locate the failure before changing the system. If a task fails because retrieval returned irrelevant evidence, rewriting the final-response prompt may not help. If the right result was reached through a prohibited operation, improving answer quality is not the fix. Trace grading turns “the score went down” into a more specific investigation of routing, tool use, policy enforcement, or outcome verification.
Run evaluations whenever behavior can change
Connect the evaluation suite to changes that may affect agent behavior, such as prompts, routing, tools, model configuration, guardrails, or task logic. Run relevant deterministic checks and representative dataset evaluations before release, and compare results against a meaningful baseline. OpenAI recommends continuous evaluation on changes and monitoring for newly observed nondeterminism in its evaluation guidance; its agent guide describes repeatable evaluation runs for workflow comparisons.
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- Identify the change and affected tasks. Select the cases and checks relevant to the changed component, while retaining critical end-to-end cases that could expose unexpected interactions.
- Run the same evaluation definition against comparable versions. Keep the agent configuration, dataset, and grader criteria clear enough that a score difference has an interpretable cause.
- Inspect dimension-level results and failures. Look beyond the aggregate: identify affected cases, criterion-level changes, and trace evidence for meaningful regressions.
- Make a release decision using documented gates. Apply thresholds chosen for the task’s risk, and require review where a critical failure cannot be responsibly reduced to an average.
- Store the run context. Record the application or agent version, dataset version, grader or rubric version, and results so later comparisons remain meaningful.
There is no universal continuous-integration configuration in the cited guidance. Teams can implement this with custom scripts and CI checks, or use an evaluation platform that fits their workflow. The essential property is comparability: if the inputs, graders, or scoring rules changed between runs, make that visible rather than attributing the entire difference to the agent.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Combine offline testing with production monitoring
Offline evaluation uses a curated dataset to compare versions, run regression checks, and test against historical cases. It is repeatable and gives a controlled view of known tasks. Online evaluation examines live behavior to surface anomalies and cases missing from the curated set. These approaches answer different questions; neither replaces the other. LangSmith’s evaluation documentation describes offline regression and backtesting alongside online evaluation and production feedback loops.
Use production monitoring to identify candidates for review, not to assume that every unusual score is a confirmed defect. A live case may be an actual failure, a new valid behavior, a change in user mix, or an evaluator artifact. Have an appropriate reviewer examine the request, trace, and outcome; then decide whether the case should become a regression test, prompt or tool change, policy update, or no action.
When a case is worth preserving, convert it into an offline example with the relevant expected outcome and evaluation criteria. That closes the loop: a failure observed in deployment can be checked against subsequent changes before those changes reach users. Make case additions and grader revisions visible in version history so score movement is not mistaken for an application regression when the evaluation itself changed.
Account for run variability and grader error
Agent runs may vary, so one successful or failed run is not always representative. For important or unstable tasks, run multiple trials and inspect the spread of outcomes rather than treating one score as definitive. Anthropic’s agent evaluation article describes multiple trials as a way to obtain more consistent results when outputs vary.
Repeated trials are especially useful when a task is nondeterministic, but they do not repair a bad rubric or an unrepresentative dataset. Check that each grader measures the stated objective. A threshold that rewards the wrong proxy can produce a stable, repeatable score and still encourage the wrong behavior. Review borderline cases and disagreements between automated judgments and people, and refine criteria when evidence shows that the grader and task are misaligned.
Calibrate automated graders with human or expert judgments, particularly for subjective or high-stakes criteria. Subject-matter experts can provide annotations where general evaluators lack the knowledge needed to assess a domain-specific result. OpenAI’s dataset guide discusses annotations and expert input. Keep human review in the loop where an error has material consequences, rather than treating automation as a substitute for an appropriate decision-maker.
Choose an implementation that fits the workflow
A small custom harness can be a reasonable starting point when checks are deterministic and the evaluation surface is limited. It gives a team direct control, but the team must maintain whatever dataset handling, grader orchestration, trace capture, and reporting it needs. Evaluation and observability platforms may combine some of those functions, but feature fit depends on the workflow. Anthropic names Braintrust, LangSmith, Langfuse, Phoenix, and Arize AX as examples of frameworks and platforms; that list is not an endorsement or a claim that one is right for every team.
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|---|---|---|
| Custom scripts and CI checks | The checks are clear, the evaluation set is manageable, and the team wants a small, directly maintained harness. | The team owns ongoing work for any dataset, reporting, grader, or trace features it needs. |
| Evaluation or observability platform | Integrated traces, datasets, experiments, or online monitoring would materially reduce operational effort. | Verify that integrations, grader inspection, hosting, and data handling match requirements before adopting it. |
Compare options on their integration with the existing agent framework and model providers; support for offline datasets and experiments; production trace capture and online evaluation; data residency and self-hosting needs; grader flexibility and inspectability; and the effort of adopting a platform versus maintaining a custom harness. Actual current capabilities should be checked in each provider’s documentation before a decision. A platform cannot compensate for vague success criteria or a dataset that fails to represent the tasks at stake. Anthropic notes that tools are useful only when evaluation tasks and graders are well designed, and that teams may combine tools, build their own framework, or start with simple scripts.
Check OpenAI Evals lifecycle dates before building on it
As of October 13, 2026, the OpenAI dataset guide says the Evals platform is scheduled to become read-only for existing users on October 31, 2026, and to shut down on November 30, 2026. The same guide points to Evals for advanced features such as external-model evaluation, API interaction, or larger-scale runs. These are near-term, time-sensitive dates, so check the official dataset and Evals transition guidance before making an implementation decision that depends on the platform. This lifecycle note is specific to that service; it does not change the broader evaluation practices described above.
Quick Recap
A practical readiness checklist
- Each evaluated task has an observable success condition, with outcome and process checks distinguished where they matter.
- The dataset covers representative, difficult, and reviewed failure cases, and its version is recorded.
- Each grader has an explicit criterion, and deterministic checks are used for rules that can be asserted directly.
- Aggregate scores do not hide critical failures; component results and release gates are documented.
- Traces retain enough information to diagnose tool choice, handoffs, guardrails, intermediate results, and end state.
- Relevant evaluations run on behavior-affecting changes, and comparisons record application, dataset, and grader versions.
- Production cases are reviewed before they become offline tests, and automated graders are checked against human or expert judgment where appropriate.
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