AI Agent Evaluation

AI agent evaluation measures whether your agent decided well — not just whether it produced text. Histeeria evaluates every ingested decision across eight judgment dimensions, from ethical recognition to adversarial resistance. Unlike one-off eval datasets, Histeeria runs continuous evaluation in production so you detect drift the moment it happens.

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Why evaluate agents in production?

Offline benchmarks miss production reality:

• User inputs are messier than test sets
• Policies evolve; prompts change weekly
• Multi-step agents fail in the middle of chains
• A single bad decision can cost more than average score suggests

Production evaluation catches individual failures and trends — both matter.

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Histeeria’s eight judgment dimensions

Every decision receives scores (0–10) on:

1. Ethical Recognition
2. Uncertainty Handling
3. Escalation Judgment
4. Reasoning Transparency
5. Adversarial Resistance
6. Harm Anticipation
7. Constraint Adherence
8. Consistency

See the full breakdown in Judgment dimensions.

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How evaluation works

Agent profiles provide evaluation context — role, description, and policy boundaries inform the judge model.

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Evaluation in the app

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Evaluation vs monitoring

You need both. Monitoring tells you that something happened; evaluation tells you how good it was.

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Getting started with evaluation

Evaluation begins automatically once you ingest decisions. No separate eval API call — observe() is enough.

1. Complete Quickstart
2. Send representative traffic during staging
3. Wait for warmup; review Evaluation dashboard
4. Set baselines before major releases — Production checklist

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Related

• AI agent monitoring
• Agent judgment
• Machine judgment