AI Evaluations
Evaluate Agents With Scores
Your Risk Team Can Trust
Score agents before release and in production with LLM judges, agent judges, and code checks, then see how closely they match your reviewers.
Language models can evaluate agent behavior quickly and at scale. But without comparison against domain expert assessments, teams cannot establish how reliable those scores are. A few screenshots are insufficient when validation requires evidence.
Lumenova AI applies evaluators to agent traces and records reviewer assessments for the same cases. This lets teams measure agreement, examine discrepancies, and provide a clear record of how each score was reached.
As evaluation criteria evolve, risk teams can track whether agreement improves and identify where human review is still needed.
Capabilities
Evaluate Every Stage of Agent Performance
Test changes before they ship, score live traffic, and show validators how well the automated scores hold up.
Evaluator Types
Score with LLM-as-judge, agent-as-judge, code checks, and system probes, alone or grouped into suites you can export and import as JSON.
Dataset Experiments
Compare prompts, models, and pipelines on the same dataset, with per-record results and a statistical comparison. Rerun only failed cases.
CI Gates
Run evaluations from CI with the SDK and fail the build on the scores you choose. You add the step to your own CI system.
Production Scoring
Evaluate live traces automatically by filter and sample rate, so quality is measured on real traffic and issues surface as they happen.
Reviewer Agreement
Measure how closely the judge matches blind reviewer scores. See agreement by rubric, with results withheld for small samples.
Reviewer Consistency
Compare two review queues to check how consistently your own people score before treating them as the benchmark.
Judge-Reviewer Gaps
Open the largest gaps between reviewers and the judge, each linked to its trace with the disputed span selected.
Sample records
Store how each review sample was drawn, so the next validation cycle can see exactly which items were checked.
The Evaluation Loop
- Platform
Collect
Production traces arrive by filter and sample rate, or you build a dataset.
- Platform
Score
LLM judges, agent judges, and code checks score each item.
- Your reviewers
Review
Reviewers score a sample without seeing the automated score.
- Platform
Compare
Agreement is reported per rubric score, and each disagreement opens its trace.
- Your CI
Gate
Your pipeline fails the build on the scores you choose.
Frequently Asked Questions
Your reviewers score the same items blind, and Lumenova AI reports how closely the judge agrees with them for each rubric score. When the sample is too small to support a reliable number, agreement is withheld rather than overstated.
LLM-as-judge for subjective quality, agent-as-judge for reviewing an agent’s full reasoning and tool use, code checks for deterministic rules like format or schema, and system probes for testing live behavior. Use them alone or group them into suites you can export and import as JSON.
Compare two review queues to see how consistently your people score. Checking that first means the benchmark you measure the judge against is itself dependable.
Yes. Set filters and a sample rate to control which live traces are scored, balancing coverage against cost while still measuring quality on real traffic.
Lumenova AI stores how each review sample was drawn, along with agreement results and disputed cases linked to their traces. The next validation cycle can see exactly which items were checked and how.