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Turn raw agent logs into a reliable evaluation dataset by treating every run as a versioned, reviewable trajectory—not just a final answer. Capture the complete execution, normalize and redact it, attach explicit expectations, sample for coverage, version the resulting examples, and run repeatable evaluations that score both the outcome and the path taken.
This approach reveals failures that answer-only tests miss: the wrong tool chosen, unsafe arguments, a missed handoff, a policy violation, or a poor recovery after an API error.
What an agent-trace dataset contains
An agent trace is the end-to-end record of an agent run. It can include the user request, model calls, tool calls, handoffs, guardrail decisions, observations returned by tools, intermediate outputs, the final response, errors, timing, token or cost data, and the environment state in which the run occurred.
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A dataset is created when those traces are converted into stable examples with a task definition, expected behavior, labels or scores, and provenance. The resulting record should let another person answer four questions:
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- What was the agent asked to do?
- What did it do at every meaningful step?
- Which behaviors were acceptable or unacceptable, and why?
- Can the same example be evaluated again after a prompt, model, tool, or policy change?
The distinction matters because a correct final answer can hide a dangerous process. An agent may reach the right result after calling an unauthorized tool, leaking data in an argument, ignoring a required approval, or retrying an operation until it happens to work. Conversely, a run can have a sensible trajectory but fail because an external service was unavailable. A useful dataset preserves enough context to distinguish those cases.
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 that idea as the operating definition, while deciding which internal details are safe and necessary to retain for your own system.
The six-stage trace-to-dataset pipeline
- Capture: record the complete execution, including tools, handoffs, controls, observations, errors, and environment details.
- Normalize and redact: map events to a stable schema, remove or mask secrets and personal data, and retain an immutable source record.
- Annotate expectations: define task-level success and step-level checks for tools, arguments, policy, handoffs, recovery, and answer quality.
- Sample for coverage: deliberately include task types, outcomes, tools, versions, trajectory lengths, and recovery patterns rather than accepting a convenient random sample.
- Version and document provenance: make every example and dataset release reproducible, with boundaries for training, validation, and test data where relevant.
- Evaluate repeatedly: run the same graders and metrics across agent versions, inspecting both final results and trajectories.
Skipping a stage creates a predictable weakness. Incomplete capture prevents diagnosis; unstructured logs make labels inconsistent; unbalanced sampling inflates apparent reliability; missing provenance makes regressions impossible to reproduce; and answer-only scoring rewards unsafe shortcuts.
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Stage 1: Capture complete, ordered traces
Record events, not just messages
Store a run as an ordered event stream. At minimum, distinguish model invocations, tool requests, tool results, handoffs between agents or workflows, guardrail decisions, user-visible messages, failures, retries, and termination. A single text transcript is difficult to grade because it hides which text was an instruction, an observation, a tool argument, or an answer.
Each event should have a run identifier, a unique event identifier, a parent or causal identifier, an event type, a timestamp, and a payload. Parent identifiers allow you to reconstruct nested calls and parallel branches. Use a monotonic sequence number as well as wall-clock time so ordering remains unambiguous when clocks differ across services.
Capture the surrounding state
The same prompt can produce different behavior when the available tools, permissions, records, feature flags, or external data change. Record the task and environment identifiers, tool and schema versions, model and prompt versions, policy or guardrail versions, locale, and relevant configuration. For mutable systems, store an environment snapshot or a stable reference to one.
Include latency for each event and for the complete run. If your accounting permits it, record token counts, estimated cost, retry count, queue time, and rate-limit responses. These fields support efficiency and reliability analysis without changing the correctness labels.
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Do not retain only successful transcripts. Tool timeouts, malformed arguments, permission denials, hallucinated resources, contradictory observations, and abandoned handoffs are often the most valuable examples. Record the error class, the raw service status where safe, whether the agent retried, what changed between attempts, and how the run ended.
Separate sensitive payloads from evaluation metadata
Secrets should never be copied into a benchmark merely because they appeared in a production trace. Capture a typed reference to a protected object when the content is not needed for grading. If content is needed, apply a documented redaction or replacement procedure before annotators and graders can access it. Never treat a redacted string as equivalent to the original without recording that transformation.
A practical event schema
A stable schema makes traces queryable and lets graders work across models and products. The following fields are a useful baseline; add domain-specific fields rather than overloading a generic text field.
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| Field | Purpose | Example values |
|---|---|---|
run_id |
Groups every event in one execution | run_2026_10_18_0042 |
event_id and parent_id |
Reconstructs causal and nested structure | evt_17, parent evt_12 |
sequence and timestamp |
Preserves order and wall-clock timing | 31, ISO 8601 timestamp |
event_type |
Identifies the operation | model_call, tool_call, tool_result, handoff, guardrail, error, final |
task_id and task_family |
Supports stratified analysis | refund_approval, customer_support |
agent_version, prompt_version, model_version |
Explains version-to-version changes | Immutable release identifiers |
tool_name and tool_version |
Identifies the external capability used | inventory_lookup, schema version 3 |
input and output |
Stores the request and observation, subject to redaction | Structured JSON or typed text |
status and error_class |
Distinguishes success, refusal, timeout, and other failures | success, timeout, invalid_argument |
latency_ms, tokens, estimated_cost |
Measures operational impact | Numeric values with their accounting definition |
redaction_policy |
Records what transformation was applied | Policy identifier and revision |
Keep the raw event envelope immutable. Produce a separate evaluation view with normalized fields and redacted payloads. This two-layer design lets an authorized auditor explain how an example changed without exposing production secrets to graders.
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Use deterministic identifiers
Generate stable identifiers for tasks, tools, policies, and dataset examples. Do not use a display name that changes when a team renames a prompt. A run can have a human-readable label in addition to an immutable identifier.
Normalize equivalent events
Different SDKs may represent a tool invocation as a message, a span, or a nested request. Map those representations into one canonical event type while preserving the original provider fields in an extension object. Normalize timestamps to a single standard, represent durations in a single unit, and define how parallel calls are ordered.
Redact systematically
Document rules for credentials, access tokens, payment data, health information, personal identifiers, private conversation content, and customer-specific records. Prefer typed replacements such as <EMAIL_REDACTED> over deleting surrounding context. A grader can then verify that an agent selected the right field without seeing the actual address.
Redaction must be deterministic when possible. If the same entity appears in several events, replace it consistently within that run so tool arguments and observations remain comparable. Record whether a label was human-reviewed, automatically detected, or manually corrected.
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Check the transformed dataset
- Verify that every event has a run and sequence identifier.
- Check that parent references resolve and that terminal events are present.
- Search for credentials and personal data after redaction, not only before it.
- Confirm that malformed tool arguments remain visible enough to grade.
- Compare counts and basic distributions between the immutable source and evaluation view.
Stage 3: Define what “good” means before labeling
Write task-level success criteria
State the outcome in observable terms. “Help the customer” is too broad; “issue a refund only when the order is eligible, record the decision, and explain the result” can be graded. Specify acceptable alternatives when several workflows are valid, and identify hard failures that override a high final-answer score.
Add step-level expectations
Useful checks include:
- Tool selection: Was the authorized tool appropriate for the task?
- Argument validity: Were required fields present, types correct, and values within policy?
- Data minimization: Did the call avoid sending fields the tool did not need?
- Handoff: Did the agent transfer to the right specialist when authority or context required it?
- Guardrails: Did it follow refusal, approval, and escalation rules?
- Recovery: After an error, did it retry safely, change strategy, ask for clarification, or stop?
- Final response: Was the answer factually correct, complete, and appropriately calibrated?
Represent each check as a label, score, or structured finding with evidence pointing to one or more events. “Failed tool use” is less actionable than “selected delete_record instead of read-only lookup_record at event 8.”
Separate hard constraints from quality preferences
A policy violation, unauthorized side effect, or fabricated confirmation may be a release-blocking failure even when the prose is excellent. Tone, brevity, and formatting can be graded separately. Define the aggregation rule explicitly—for example, a run cannot pass if any critical safety check fails, while noncritical quality scores are averaged.
How to annotate traces consistently
Combine automated checks with human review
Deterministic validators are best for schema compliance, required fields, permission checks, and exact state transitions. Model-based graders can classify nuanced behavior such as whether a recovery was reasonable, but they need a rubric, examples, and periodic calibration. Human reviewers should inspect a sampled set, especially for new task families and disagreements between automated judgments and observed outcomes.
Use evidence-linked labels
Require annotators to select the event or span supporting a label and to distinguish “not observed” from “failed.” If a tool was never needed, tool-selection quality may be not applicable rather than incorrect. If a downstream service returned no data, final-answer correctness may be indeterminate rather than a failure attributable to the agent.
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Measure reviewer agreement
Give multiple reviewers an overlapping calibration sample and track agreement by label. Investigate systematic disagreements by revising the rubric, adding counterexamples, or splitting an ambiguous criterion into smaller checks. Agreement is not proof that a rubric is correct, but low agreement is a warning that the criterion is not operationally clear.
Stage 4: Sample for coverage instead of convenience
A dataset made mostly of short, successful, single-tool runs will make an agent look safer and more capable than it is. Build sampling strata before selecting examples. Useful dimensions include task family, difficulty, outcome, tool combination, trajectory length, model version, prompt version, environment, language or locale, and recovery pattern.
Include the full outcome spectrum
- Successful runs with a clean trajectory
- Successful outcomes reached through inefficient or risky steps
- Partial successes and correct refusals
- Tool and environment failures
- Policy violations and unsafe attempts
- Ambiguous tasks requiring clarification
- Runs that terminated early, timed out, or exceeded a budget
Use deliberate edge cases
Manually created examples are valuable for rare but consequential conditions: conflicting instructions, stale records, permission changes mid-run, duplicate tool results, adversarial inputs, and recovery after an irreversible operation is unavailable. Keep these examples marked as constructed so they are not mistaken for naturally occurring frequency estimates.
How many traces are enough?
There is no universal minimum number of traces or annotators established for every agent. The required size depends on task risk, expected variance, the precision of confidence intervals, the number of strata, and how small a regression must be detected. High-risk tasks need more coverage of rare failures than low-risk tasks, even when their average pass rate is high.
Choose a target detectable change and a confidence level, then estimate the sample needed for the relevant metric. For a proportion such as pass rate, report the estimate with a confidence interval rather than presenting a single percentage as exact. Ensure every important stratum has enough examples to produce a useful interval; an overall sample can be large while a critical edge case has only one run.
Stage 5: Version examples and preserve provenance
Treat each dataset release as a reproducible artifact. At the example level, record:
- Run and task identifiers
- Task specification and expected outcome
- Agent, prompt, model, tool, policy, and environment versions
- Capture and transformation timestamps
- Outcome and every grader result
- Grader identity or rubric version
- Redaction and normalization policy versions
- Whether the example is production-derived, synthetic, or manually authored
- Dataset split and eligibility rules
Maintain a changelog for additions, removals, relabeling, schema changes, and redaction fixes. Never silently overwrite a label: create a new dataset revision and retain the prior result for comparison.
Keep evaluation boundaries clean
If examples are used for prompt tuning or model development, keep a protected validation or test set that is not repeatedly optimized against. Prevent near-duplicates from crossing splits; otherwise a system may memorize the task wording or data rather than learn a robust behavior. Record the split assignment as metadata so a later export cannot reshuffle it accidentally.
Stage 6: Evaluate outcomes and trajectories together
Run a fixed evaluation suite against each candidate agent version and store the complete run metadata with the scores. OpenAI recommends beginning with representative traces and graders while debugging, then moving to datasets and evaluation runs for repeatable benchmarking, prompt comparison, and regression measurement.
Report the essential metric groups
| Metric group | Questions it answers |
|---|---|
| Final task correctness | Did the agent produce the required result or an appropriate refusal? |
| Tool choice and arguments | Did it use an authorized capability with valid, minimal parameters? |
| Policy and guardrail adherence | Did it respect approval, privacy, safety, and escalation rules? |
| Handoffs | Did it transfer responsibility at the right time and preserve necessary context? |
| Recovery | Did it respond safely and effectively to errors, timeouts, and contradictory observations? |
| Efficiency | How long was the trajectory, and what latency, token, or cost overhead did it incur? |
| Consistency | Does performance hold across task and failure strata rather than only in aggregate? |
Google Cloud’s generative-AI evaluation service supports trajectory-evaluation and final-response-evaluation metrics in one SDK query. That combination reflects a general design principle: process and outcome should be measured separately, then related in analysis. A final answer score cannot tell you whether the agent took an unsafe path; a trajectory score cannot by itself establish that the user’s requested result was delivered.
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Inspect disagreements and outliers
Route cases where automated graders disagree with observed state, human labels, or deterministic validators to review. Examine both false positives and false negatives. Also inspect unusually long trajectories, high-cost passes, repeated retries, and short runs that appear successful despite skipping expected controls.
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Tool-selection tests
Create tasks where multiple tools appear superficially plausible but only one is authorized or semantically correct. Grade the selected tool, the reason it was needed as reflected in the trace, and whether the agent avoided an unnecessary side effect. Include read-only and mutating variants so a generic “tool was called” score cannot pass an unsafe choice.
Argument tests
Validate required fields, data types, ranges, authorization context, and consistency with the user’s request. A valid JSON shape is not enough: an agent can submit a syntactically correct account identifier belonging to the wrong customer. Compare arguments with the task state and policy, not only with the tool schema.
Recovery tests
Inject controlled failures such as timeouts, rate limits, malformed responses, stale versions, and permission denials. Define the acceptable response for each class. A transient timeout may permit a bounded retry with the same idempotent request; a permission denial may require escalation; a duplicate-charge warning should stop further mutation. Grade whether the agent recognized the failure, selected a safe next action, avoided compounding damage, and communicated uncertainty.
Handoff tests
Test both premature and delayed handoffs. The trace should show why the transfer occurred, which context was passed, and whether the receiving workflow had the authority and information to continue. A handoff that merely moves an unresolved problem to another agent should not receive full credit.
Public benchmarks and software approaches
Public resources can supplement an internal dataset, but none is a universal substitute for task-specific validation. Compare options using schema richness, human and automated labeling, provenance and versioning, trajectory versus final-answer metrics, reproducibility, privacy controls, observability integration, portability, and operating cost.
| Approach | What it contributes | What to verify for your use case |
|---|---|---|
| OpenAI trace-to-grader-to-dataset workflow | Integrated capture, trace grading, dataset creation, and repeatable evaluation concepts | Whether your redaction, policy, and environment requirements are represented in the exported records |
| MLflow trace-derived evaluation datasets | Uses existing traces to create datasets and lets teams add ground-truth expectations before or after examples enter a dataset | How your custom event fields, labels, and lineage map into the implementation you operate |
| Google Cloud evaluation service | Trajectory and final-response evaluation metrics available in one SDK query | Metric definitions, data residency, and integration with your existing trace store |
| ATBench trajectory benchmark | The current ATBench release contains 1,000 trajectories, each a complete execution trace with user requests, agent responses, tool calls, and environment feedback | Whether its safety-oriented tasks match your tools, risk model, and production distribution |
A 2026 Springer Nature review identifies three broad dataset types: training datasets, benchmark databases, and trajectory databases. The third category is particularly useful for post-hoc analysis and practice because it preserves histories of agent-environment interaction rather than only prompts and answers.
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Start with a narrow, high-value task family and make the data contract explicit. A normalized example can look like this:
{
"example_id": "refund_0042_r3",
"source_run_id": "run_2026_10_18_0042",
"task": {
"id": "refund_approval",
"family": "customer_support",
"spec_version": "7"
},
"versions": {
"agent": "agent_12",
"prompt": "prompt_31",
"model": "model_release_id",
"tools": ["orders_v4", "refunds_v2"],
"policy": "policy_9"
},
"events": [
{"sequence": 1, "type": "model_call", "input": "<REDACTED>"},
{"sequence": 2, "type": "tool_call", "tool": "orders_v4", "arguments": {"order_id": "<ORDER_ID>"}},
{"sequence": 3, "type": "tool_result", "status": "success"},
{"sequence": 4, "type": "final", "status": "success"}
],
"expectations": {
"task_success": true,
"required_checks": ["eligibility_verified", "authorized_mutation", "clear_confirmation"]
},
"labels": {
"tool_choice": "pass",
"argument_validity": "pass",
"recovery": "not_applicable",
"final_quality": 4
},
"provenance": {
"source": "production",
"redaction_policy": "redact_5",
"rubric_version": "rubric_11",
"split": "test"
}
}The exact field names are less important than consistency. Validate the schema at ingestion, reject incomplete terminal events, and make transformations idempotent so reprocessing a run does not create a different example.
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- Select one task family with a clear outcome and known failure modes.
- Capture several weeks or a deliberately constructed set of representative runs, including failures.
- Normalize events and run automated secret and personal-data detection.
- Write a rubric with hard constraints, quality scores, and “not applicable” states.
- Have reviewers label an overlap sample and resolve disagreements.
- Freeze a versioned test slice before tuning the agent against the remaining data.
- Run baseline evaluations, publish distributions by stratum, and record the grader and environment versions.
- Add new failure patterns to a subsequent release rather than silently changing old examples.
Common failure modes and fixes
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Symptom: pass rates look high, but incidents cannot be explained. Fix: capture tool requests, results, controls, handoffs, errors, and environment references as first-class events.
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Successful runs dominate the dataset
Symptom: a new agent appears reliable while failing in production edge cases. Fix: stratify by outcome and deliberately add unsuccessful, ambiguous, long, and recovery-heavy trajectories.
Labels lack operational definitions
Symptom: reviewers disagree or graders change behavior between releases. Fix: write observable criteria, supply positive and negative examples, link labels to evidence, and version the rubric.
Redaction removes the evidence needed to grade
Symptom: annotators cannot tell whether the right record or field was used. Fix: use typed, consistent replacements and preserve non-sensitive structure in the evaluation view.
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Dataset revisions are overwritten
Symptom: a regression cannot be reproduced because old labels or prompts are gone. Fix: publish immutable releases with changelogs and retain the source and transformation metadata.
Automated judges are trusted without calibration
Symptom: scores rise while human reviewers find unsafe behavior. Fix: maintain a human-reviewed calibration sample, measure agreement, and investigate judge-versus-outcome disagreements.
Aggregate scores hide critical strata
Symptom: the overall pass rate improves while one high-risk task family worsens. Fix: report confidence intervals and per-stratum results, and set release gates for critical checks.
Privacy, security, and operational cost
Trace datasets often contain more sensitive information than prompt-only datasets because they include records returned by tools, identifiers in arguments, and internal policy decisions. Apply least-privilege access to raw traces, separate production storage from annotation storage, encrypt both, and log access. Set retention periods based on legal and operational requirements; a benchmark should not become an indefinite copy of customer data.
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Capturing every payload can be expensive. A practical compromise is to retain complete structured events for high-risk or sampled runs, store references to large immutable artifacts, and keep lightweight metrics for runs that are not selected. Do not drop fields needed to investigate safety or recovery merely to reduce storage. State exactly how token, latency, and cost numbers are calculated so comparisons remain meaningful across providers and model versions.
Release checklist
- Every example has a stable ID, task specification, event order, and terminal status.
- Model, prompt, tool, policy, environment, rubric, and redaction versions are recorded.
- Raw records are immutable and the evaluation view is reproducible.
- Secrets and personal data are detected, transformed, and checked after redaction.
- Success criteria cover final results and process behavior.
- Tool choice, argument validity, handoffs, guardrails, recovery, latency, and cost are measurable where applicable.
- Successful, failed, ambiguous, long, and edge-case trajectories are represented.
- Training, validation, and test boundaries prevent leakage and near-duplicate contamination.
- Human reviewers calibrate on an overlap sample and disagreements are tracked.
- Automated grader results are compared with observed outcomes and human judgments.
- Dataset releases have changelogs, confidence intervals, per-stratum results, and reproducible evaluation commands or job definitions.
Bottom line
A reliable agent-evaluation dataset is a versioned record of behavior, not a pile of transcripts. Preserve the whole trajectory, make sensitive transformations auditable, define observable expectations, sample for failure and recovery, and score the path as well as the answer. With those controls in place, every new model, prompt, tool, or policy release can be measured against the same evidence instead of judged by a handful of persuasive examples.
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