Generic benchmarks
Public eval sets miss your policies, tools, and escalation paths.
Golden datasets
Golden datasets for AI agents that cannot afford generic outputs.
We turn repeated production failures into approved responses and trajectories your agent can retrieve at runtime—without fine-tuning model weights.
Most golden datasets never leave the lab.
Benchmarks and synthetic labels can score a model. They rarely capture the exceptions your humans already fix in production.
Annotation without context
Labels without the full trace cannot teach the next similar request.
Fine-tuning as the only fix
Training runs slow the correction cycle and bury individual examples.
Lost human edits
Support tickets and reviewer rewrites disappear instead of becoming reusable experiences.
What less than three builds
A golden dataset that is also an approved experience library: inspectable, scoped, versioned, and removable.
Production trace reconstruction
Requests, tool calls, intermediate steps, and final responses from OpenTelemetry or exports.
Human-reviewed corrections
Domain experts rewrite responses and trajectories with critiques tied to your rubric.
Runtime retrieval ready
Approved examples enter context as dynamic few-shots for similar future requests.
Design the dataset before you label it
A useful golden dataset is not a transcript archive. It has a narrow workflow boundary, a sampling rule, explicit acceptance criteria, and provenance for every approved example.
Sampling plan
Start with recurring, costly intents rather than a random export. Preserve difficult edge cases and known failure modes.
Annotation protocol
Define what reviewers can change, when they escalate, and how an approved answer is distinguished from a plausible one.
Provenance and versions
Keep the original trace, correction, reviewer, policy scope, and version together so each record can be audited or retired.
Use real failures as the source material in a production-trace golden datasetMeasure whether the library improves an agent through LLM evaluation services
Who this is for
- Customer support and voice agents with repeating failure modes
- Healthcare admin, claims, and compliance review workflows
- Teams that already edit agent output and need those fixes to compound
- Operators who need measurable before/after—not another dashboard
Agent Improvement Sprint deliverables
A — Audit
Trace audit, failure patterns, and an annotation rubric tied to your success definition.
B — Dataset
Human-corrected responses/trajectories plus a retrieval prototype for one workflow.
C — Proof
Baseline comparison, quality review, cost/latency notes, and a rollout recommendation.
Controls that keep retrieval safe
Irrelevant or stale examples can hurt performance. Approval gates, metadata filters, and thresholds come before rollout.
Related
Request a sample golden dataset path—or book a trace review.
Send a slice of production traces. We will estimate experience coverage and recommend one focused sprint.