How Do You Test an AI Agent? A Look at Harness AI Evals

Harness AI Evals is presented as a platform for testing non-deterministic AI agents through a unified evaluation system that uses the same datasets, metrics, and scoring logic before deployment and in production. Users define evaluations through targets such as prompts or endpoints, versioned datasets of test cases, quality metrics including deterministic checks, LLM-as-a-judge rubrics, safety scoring, and multi-step trajectory analysis, plus thresholds that can block or advise on releases. Evaluations can be grouped into suites and integrated as native CI/CD pipeline quality gates, allowing regressions to fail builds without custom scripts. An example customer-support response demonstrates how an answer can sound polite while failing relevance-related metrics, preventing deployment. The platform also emphasizes enterprise features including role-based access, policy governance, audit trails, secrets management, SSO, data residency, versioned registries, and token-cost tracking, while planned capabilities include Git-backed configurations, production-trace observability, automated dataset creation, drift detection, rollback, prebuilt suites, and human annotation workflows.

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