For Model Risk, Validation, and AI RISK Teams
Validate AI Agents With Evidence
You Can Reproduce
Test automated scores against your own reviewers, read the rules that limit each agent, and reproduce any decision from its recorded input.
Why Agent Validation Needs Better Evidence
Validators need evidence they can inspect, challenge, and reproduce.
Agents often leave gaps in that evidence, making it difficult to justify approval.
Unverified Automated Scores
Teams use language models to grade agents without measuring how closely those scores agree with domain experts.
Controls Hidden in Code
Limits on agent actions are buried in application logic or depend on another model’s judgment, making them difficult for validators to inspect and test.
Decisions Reconstructed After the Fact
Explaining why an agent acted means piecing together scattered logs, often without a clear record of the rule applied or the input evaluated.
From Testing to Sign-Off
Evidence Built for Effective Challenge
Benchmark automated scores against your reviewers, test each agent’s controls, and document every step so your findings can be reproduced and defended.
Blind Reviewer Scoring
Have reviewers score a sample without seeing the automated score. Agreement is reported per rubric and withheld when the sample is too small.
Reviewer Consistency
Compare two review queues to confirm your own people score consistently before treating them as the benchmark.
Disputed Cases
Open the largest gaps between reviewers and automated scores, each at the trace step in question.
Sampling Records
Store how each review sample was drawn, so the next cycle sees which items were checked and how they were chosen.
Policy History and Replay
Review each policy’s author, timestamp, and change note, and replay past decisions to test how a proposed rule would decide.
Tiered Approvals
Record each use case’s owner, approver, risk tier, and approval status, and let the approval set what its agents can reach at the AI Gateway.
How It Starts
- Your team
Pick the traces
Traces from one agent you need to validate.
- Platform
Score
The platform scores them with its own evaluators.
- Your reviewers
Review
Your reviewers score a sample against your rubric, without seeing the automated score.
- Together
Compare
See where the scores match and where they part ways. You keep the agreement figures, the disagreements, and how the sample was drawn.
Frequently Asked Questions
Reviewers score samples blind, without seeing the automated score, and pull decision records themselves rather than requesting them from engineering. The audit log is append-only by application design.
Agreement is withheld when the sample can’t support it, so a weak number never looks like a strong one.
Yes. Each recorded decision keeps the policy, outcome, reason, and exact input, and each policy keeps its full version history. You can load a past decision with its input to see how the rule decided it, or how a proposed rule would. Records are kept for the retention period you configure.
Pinned policy versions and versioned registry artifacts show exactly what was running at any point, and production traces can be scored continuously on a sample you control.
Yes. Validators can probe running agents as a black box, compare approaches on their own datasets, and replay past decisions, without code access or engineering support.