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Turn agent traces into a reliable evaluation dataset by building a six-stage pipeline: capture complete executions, normalize and redact them, attach explicit expectations, sample for coverage, version every artifact, and run repeatable evaluations. A useful dataset preserves not only the final answer but also tool choices, arguments, handoffs, guardrail decisions, observations, errors, latency, and environment state.
The result should let you answer two different questions for every task: Did the agent achieve the required outcome? and Did it get there in a safe, valid, efficient, and recoverable way? Treating those as separate measurements is the difference between a trace archive and an evaluation system.
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What an agent trace contains—and why it belongs in an evaluation dataset
An agent trace is the time-ordered record of one execution from the initial request to the final response. It may include multiple model calls, tool calls, observations returned by tools or an environment, agent-to-agent handoffs, guardrail decisions, intermediate outputs, errors, retries, latency, token or cost measurements, and the state of the environment in which the run occurred.
A final-answer-only log cannot explain whether a correct result was produced safely or whether an apparently good answer was a lucky outcome. Two runs can return the same text while differing materially in tool permissions, data access, number of retries, policy violations, or recovery from an outage. A trajectory dataset keeps those distinctions available for analysis.
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Trajectory databases are one of three broad dataset types identified in a 2026 Springer Nature review: training datasets, benchmark databases, and trajectory databases. Training data teaches a model; a benchmark provides fixed tasks and comparison rules; a trajectory database preserves histories of agent–environment interaction for analysis and practice. A production evaluation set can combine characteristics of the latter two without becoming training data.
The six-stage trace-to-dataset pipeline
1. Capture complete, ordered traces
Instrument the agent runtime at the boundaries where decisions and side effects occur. Capture the original event payload before a formatter, summarizer, or observability service can discard details. Keep events in a deterministic order and make it possible to reconstruct parent-child relationships between a model call, its tool calls, and the resulting observations.
At minimum, record these event classes:
- Request and task context: the user request, task-family identifier, task specification, expected constraints, and any permitted tools.
- Model activity: model identifier and version, prompt or instruction version, input and output messages, structured tool-call proposals, finish status, latency, and token or cost measurements when available.
- Tool activity: tool name and version, validated arguments, invocation time, return value or error, authorization result, timeout, retry count, and side-effect status.
- Handoffs: source and destination agent, handoff reason, transferred context, and whether the destination accepted or rejected the work.
- Guardrails: policy or safety checks, their decisions, blocking or modification actions, and the rule or policy version.
- Environment observations: database or application state exposed to the agent, external events, simulated-world feedback, and state changes caused by tools.
- Outcome: final response, task success or failure, evaluator-visible artifacts, unresolved errors, and termination reason.
Do not silently replace an error with a later successful retry. Store both events and connect them with a retry or recovery identifier. Likewise, preserve intermediate tool outputs even when the agent later summarizes them; the summary alone is not enough to audit an argument or a policy decision.
A practical event envelope
Use a stable envelope around every event so downstream code does not need a different parser for each model or tool. A typical record contains:
{"run_id":"…","event_id":"…","parent_event_id":"…","sequence":12,"timestamp_utc":"…","event_type":"tool_call","task_id":"…","agent_version":"…","prompt_version":"…","model_version":"…","tool_version":"…","payload":{},"status":"success"}The field names are a design choice; the invariants are not. IDs must be unique and stable, timestamps should use one timezone (UTC is the least ambiguous choice), sequence numbers must allow deterministic replay of the record, and the schema itself needs a version. Store the environment or fixture identifier alongside the run, because a tool result without the state that produced it is difficult to reproduce.
2. Normalize and redact without destroying auditability
Normalize provider-specific logs into a common schema while retaining the original immutable record. The normalized view should make equivalent events comparable across models, prompt versions, and tools. Keep a transformation manifest that records which fields were renamed, removed, hashed, masked, or transformed.
Use two linked representations:
- Raw record: access-controlled, immutable evidence retained for the shortest period that policy and incident response require.
- Evaluation view: redacted or pseudonymized content used by annotators, automated graders, and routine analysis.
Redact secrets, access tokens, authentication headers, payment data, personal identifiers, private messages, and any sensitive tool output before a trace reaches a shared dataset. A deterministic replacement such as USER_17 can preserve co-reference and test whether the agent handled identity correctly, while one-way hashing can preserve equality checks without exposing the original value. Do not use a redaction process that changes the task’s meaning without recording that fact.
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3. Define expectations at task and step level
Labels are useful only when they express an expectation that someone could apply consistently. Attach a task-level rubric for the required outcome, then add step-level checks for the path taken.
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- Task success: Was the requested result complete, correct, and within the stated constraints?
- Tool selection: Was the chosen tool authorized and appropriate for the subtask?
- Argument validity: Were required fields present, types valid, values within allowed ranges, and identifiers correctly scoped?
- Policy adherence: Did the agent follow safety, privacy, access-control, and escalation rules?
- Handoff quality: Was the handoff necessary, correctly routed, and supplied with sufficient context?
- Recovery: After an error, did the agent diagnose the failure, choose a safe alternative, and avoid repeating an invalid action?
- Efficiency: Was the trajectory’s length, latency, and resource use acceptable for this task class?
OpenAI defines trace grading this way: “Trace grading is the process of assigning structured scores or labels to an agent’s trace—the end-to-end log of decisions, tool calls, and reasoning steps—to assess correctness, quality, or adherence to expectations.” Use a rubric that turns that definition into observable criteria rather than asking a judge for an unexplained overall impression.
For each criterion, specify allowed labels, pass conditions, severity, and evidence location. A tool-argument check might be binary when a wrong account identifier is always unacceptable, while final-answer quality may use an anchored ordinal scale with examples for each level. Record the rubric version with every label so that a score remains interpretable after the rubric changes.
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A dataset made mostly of successful, short, single-tool runs will overstate reliability. Build sampling strata before selecting examples. Useful axes include task family, success or failure, tool combination, trajectory length, model version, prompt version, environment or fixture, and recovery pattern.
Include at least these categories:
- Representative ordinary tasks that reflect actual traffic.
- Successful runs, including efficient and inefficient successes.
- Failures caused by invalid arguments, unavailable tools, timeouts, stale state, permission denials, or policy blocks.
- Near misses in which the final answer appears acceptable but the path violated a rule or exposed unnecessary data.
- Long and branching trajectories, not only the median-length path.
- Tasks requiring handoffs, retries, or multi-tool coordination.
- Deliberate edge cases written manually when naturally occurring logs do not contain enough of them.
MLflow documents using existing traces to create evaluation datasets and adding ground-truth expectations either before or after examples enter the dataset. Manually created examples are particularly useful for deliberate edge cases. Keep the sampling rule with the release so another team can understand why a run was included.
5. Version examples and provenance as a reproducible artifact
Every example should carry enough metadata to explain where it came from and whether it is comparable with another example. Record:
- Run ID, task ID, and dataset-example ID.
- Task specification and task-family label.
- Agent, prompt, model, tool, policy, and grader or rubric versions.
- Environment snapshot, fixture or seed, and relevant configuration.
- Start and end timestamps, outcome, latency, and cost fields.
- Annotator identity or role, adjudication status, and confidence where used.
- Redaction, normalization, augmentation, or filtering actions.
- Dataset release, changelog, and train/validation/test membership when splits apply.
Do not overwrite an example when a label is corrected. Create a new annotation revision linked to the prior one, and retain the reason for the change. A release should be immutable, checksummed if your storage system supports it, and accompanied by a changelog that identifies additions, removals, schema changes, and rubric changes.
6. Run repeatable evaluations
Start with representative traces and graders while debugging a workflow. Once the rubric and instrumentation are stable, move to datasets and evaluation runs so prompt or model changes can be benchmarked repeatedly. Each run should record the exact dataset release, agent configuration, grader versions, execution environment, and timestamp.
Compare distributions, not only a single aggregate score. A change that improves final-answer correctness may also increase unauthorized tool calls, latency, or cost. Report results by task stratum and show confidence intervals or another uncertainty estimate appropriate to the metric.
Measure the trajectory and the final response separately
Trajectory evaluation asks whether the process was valid; final-response evaluation asks whether the delivered result met the user’s need. Google Cloud’s generative-AI evaluation service supports trajectory-evaluation and final-response-evaluation metrics in one SDK query, illustrating why both views should be first-class rather than collapsed into one score.
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| Evaluation axis | What to inspect | Evidence in the trace |
|---|---|---|
| Final task correctness | Required result, completeness, factual or transactional accuracy | Final response and task-specific checks |
| Tool choice | Whether each tool was necessary, authorized, and appropriate | Tool name, policy decision, and preceding state |
| Argument validity | Schema, types, ranges, identifiers, and least-privilege parameters | Raw and normalized arguments plus validation result |
| Guardrail adherence | Privacy, safety, access, and escalation requirements | Guardrail events, blocked actions, and policy version |
| Recovery | Diagnosis and safe behavior after errors or changed state | Error, observation, retry, alternative action, and outcome |
| Efficiency | Trajectory length, latency, retries, and cost | Per-event timing and aggregate run counters |
| Consistency | Stability across task strata, seeds, versions, or repeated runs | Grouped results across comparable examples |
Keep these metrics visible even when a task-level pass is awarded. A run can pass the business outcome while still failing a safety criterion, and a run can fail because an external service was unavailable even though the agent selected the correct recovery path. Those cases should not be indistinguishable.
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Score tool decisions in context
Judge a tool call against the state immediately before it, not against the final answer alone. Ask whether the agent had enough information to call the tool, whether a read-only operation should have preceded a write, whether the arguments reflected the latest observation, and whether the requested scope exceeded the user’s authorization. A valid schema does not make an unsafe argument acceptable.
For multi-tool tasks, grade ordering and dependency. If tool B requires an identifier returned by tool A, the dataset should retain that relationship and the grader should verify that the identifier was neither guessed nor copied from an unrelated run. Mark unnecessary calls separately from incorrect calls: both affect cost, but they signal different fixes.
Make recovery observable
Recovery is a sequence, not a single label. Represent the triggering condition, the agent’s interpretation, the next action, and the eventual outcome. Useful recovery labels include:
- Correct retry: the error was transient and the retry used bounded backoff or an allowed attempt count.
- Safe alternative: the original tool remained unavailable and the agent chose an approved fallback.
- Clarification or escalation: the agent stopped when it lacked authority or information.
- Unsafe persistence: it repeated an invalid action, widened permissions, or concealed the failure.
- Graceful failure: it reported the limitation accurately and left the environment in a known state.
Include naturally occurring failures and controlled fault cases where your test environment permits them. Record whether a failure was injected or organic; otherwise, reviewers may mistake a robustness test for a production incident.
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How many traces do you need?
There is no universal minimum number of traces or annotators established for this problem. The required size depends on task risk, expected variance, the number of strata, the width of the confidence interval you need, and how small a regression must be detected.
Choose the size by decision, not by a round number:
- List the metrics that can block a release, such as policy violations or incorrect transactions.
- Define the smallest change that matters operationally and the uncertainty you can tolerate around the estimate.
- Estimate variance from a pilot sample or prior releases, separating task strata rather than pooling unlike tasks.
- Use a power or precision calculation for the primary metric, then add enough examples to cover rare but high-severity cases.
- Reserve an untouched test set for final comparisons; do not repeatedly tune prompts against it.
Rare failures often require targeted sampling or scenario construction even when a large random sample contains none. Report the sampling design and uncertainty alongside the count. “We evaluated 10,000 runs” is not persuasive if 9,900 were easy successes and the risky stratum had only a handful.
Human review and automated graders
Automated graders make broad evaluation affordable, but they require calibration against observed outcomes. Have reviewers label a representative sample using the rubric, measure inter-rater agreement, and investigate disagreements between human judgments, automated scores, and objective task results. Agreement statistics are signals about rubric clarity, not proof that either side is correct.
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Use adjudication for high-severity disagreements and keep the adjudicated label, the original labels, and the reason for the decision. A grader that sees only the final answer cannot reliably score hidden tool misuse; a grader that sees unrestricted private data may create a privacy risk. Give each grader the minimum trace fields needed for its criteria.
Separate judge prompts, model versions, and parsing logic from the dataset. If a grader changes, rerun a fixed calibration slice and record the new version. Never compare scores from different grader versions as though they were measurements from the same instrument.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Public benchmarks and software approaches
Public resources can test portability, but they do not replace a task-specific dataset. ATBench’s current 2026 release contains 1,000 trajectories. Each sample is a complete execution trace with user requests, agent responses, tool calls, and environment feedback, making it relevant for external, safety-oriented trajectory checks. Its task mix and labels should still be compared with your own risk profile before drawing conclusions about production reliability.
| Approach | Useful capability | Important qualification |
|---|---|---|
| Integrated trace-to-grader-to-dataset workflow | Move from representative traces to structured graders, datasets, and repeatable eval runs | Rubrics and sampling still need task-specific validation |
| MLflow evaluation datasets | Derive datasets from existing traces and attach expectations before or after ingestion | Coverage, redaction, and release governance remain your responsibility |
| Google Cloud evaluation service | Evaluate trajectory and final-response metrics in one SDK query | Combined access does not eliminate the need to interpret process and outcome separately |
| ATBench trajectory benchmark | External safety-oriented comparison using 1,000 trajectories in the 2026 release | A public benchmark cannot represent every private tool, policy, or task family |
Compare approaches on schema richness, human and automated labeling, provenance and versioning, trajectory versus final-answer metrics, reproducibility, privacy controls, observability integration, benchmark portability, and operating cost. No benchmark or automated judge is universally reliable without validation against the tasks it is intended to represent.
Common failure modes and fixes
Logging only prompts and final answers
Why it fails: You cannot inspect tool selection, handoffs, policy decisions, or recovery.
Fix: Instrument every model, tool, guardrail, handoff, observation, error, and state transition, then verify trace completeness in ingestion tests.
Flattening retries into one success
Why it fails: A run that succeeded after unsafe repetition looks identical to a clean run.
Fix: Keep each attempt, link retries, and score the recovery sequence.
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Why it fails: A score change may reflect a prompt, model, tool, policy, environment, or rubric change rather than an agent improvement.
Fix: Make all of those versions mandatory metadata and release immutable dataset snapshots.
Using a single overall judge score
Why it fails: A strong final answer can hide a policy violation or excessive cost.
Fix: Report final outcome, trajectory quality, safety, recovery, efficiency, and consistency as separate dimensions.
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Removing every failed run
Why it fails: The dataset becomes a success catalogue and cannot measure reliability.
Fix: Retain failures with explicit causes, severity, and environment conditions; balance them through documented strata rather than deletion.
Redacting without a transformation record
Why it fails: A reviewer cannot tell whether a missing value was absent in the original run or removed later.
Fix: Preserve the immutable original under access control and attach a manifest to the evaluation view.
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- Write the task contract. Define success, prohibited actions, acceptable alternatives, and escalation conditions for each task family.
- Instrument the runtime. Emit ordered events for model calls, tools, handoffs, guardrails, observations, errors, and final responses.
- Validate ingestion. Reject or quarantine records with missing parents, duplicate IDs, impossible timestamps, or unpaired tool calls.
- Create the normalized view. Map provider-specific fields to a stable schema while preserving raw evidence and a transformation manifest.
- Redact and access-control. Remove secrets and personal data from annotation views; test the redactor and restrict raw-log access.
- Annotate a pilot. Apply task and step-level rubrics, calibrate reviewers, and revise ambiguous criteria before large-scale labeling.
- Build stratified releases. Include ordinary successes, failures, edge cases, long trajectories, version changes, and recovery patterns according to a documented sampling plan.
- Freeze and evaluate. Version the dataset, grader, agent, prompts, tools, policies, and environment; run the same evaluation configuration for every comparison.
- Review disagreements and regressions. Inspect human–judge disagreement, slice-level changes, and severe failures before accepting an aggregate improvement.
Release checklist
- Every example has a stable run ID, event IDs, timestamps, task family, and schema version.
- Model, prompt, tool, policy, environment, and grader versions are recorded.
- Raw and redacted representations are linked, with transformations documented.
- Successful, failed, edge-case, and recovery trajectories are represented deliberately.
- Task-level and step-level expectations are explicit and versioned.
- Human review has a calibration sample, agreement measurement, and adjudication path.
- Trajectory and final-response metrics are reported separately.
- Dataset releases are immutable, changelogged, and split appropriately for tuning and final testing.
- Results are broken down by risk-relevant strata rather than reported only as one average.
- Privacy, retention, access, and incident-response controls are tested before wider sharing.
Bottom line
A reliable evaluation dataset is not a pile of conversation transcripts. It is a versioned, privacy-aware record of agent decisions and environment interactions, paired with explicit expectations and a sampling plan that exposes both ordinary behavior and failure recovery. Capture the full trajectory, preserve its provenance, grade process and outcome separately, and size each release for the uncertainty and risk of the decision it will support.
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