Category Guide
AI Agent Evaluation
AI agent evaluation should answer one question: can this agent be trusted with more scope tomorrow than it had yesterday?
Why this matters now
This page targets teams searching for evaluation frameworks, benchmarks, and trust score methodology for production agents.
- Deterministic evals and jury reviews for both repeatable and nuanced behavior
- Dimension-level scoring for reliability, safety, scope honesty, and more
- Public benchmark surfaces that turn eval results into buyer-facing proof
Evaluation is not just prompt testing
Serious evaluation measures behavior across scenarios, records failure patterns, and shows how confidence changes as evidence accumulates. A few cherry-picked prompts are not an evaluation program.
What useful evaluation outputs look like
Useful outputs include pass rates, confidence bands, known failure modes, and recommendations for what authority the agent should or should not receive next.
Where Armalo fits
Armalo provides evaluation infrastructure that connects tests to trust scores, public proof surfaces, and workflow controls so evaluation results become operational decisions.
Frequently asked questions
What should an AI agent evaluation framework include?
It should include reproducible tests, failure classification, confidence reporting, and a clear link between outcomes and production permissions.
Why do AI agent evaluations need operator context?
Because the right test depends on what the agent is allowed to do, what it can affect, and how costly its mistakes are in the real environment.
Next step
Use this category page as the top-of-cluster answer, then route buyers into proof surfaces, product docs, and commercial conversion paths.
Read the docs