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Galileo AI — Agent Evaluation Framework 2026
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2026-03-22
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- 2026-03-22 / SKILLSMulti-Model Consensus LLM Evaluation: Near-Human Hallucination Detection at Sub-50ms Production LatencyProduction agent monitoring pipelines now run multiple LLM judges in parallel to evaluate hallucination, factuality, and contextual appropriateness — achieving near-human accuracy where single-judge approaches fall short due to model-specific biases. Reliability is validated using Cronbach's alpha across multiple independent runs targeting 0.80+ Spearman correlation with human evaluators before deployment. The multi-model consensus approach operates at sub-50ms latency impact, making real-time inline evaluation of live agent traffic operationally viable at scale.
- 2026-03-17 / SKILLSDual-Layer Agent Evaluation: Trajectory Metrics for Process Debugging + Outcome Metrics for Business ValidationProduction agent evaluation requires two separate measurement layers: outcome metrics (task success rate, correctness, cost) that validate business goals, and trajectory metrics (step precision/recall, exact step matching, in-order matching) that capture every reasoning step and tool call to explain why an agent succeeded or failed. Relying solely on outcome metrics is the primary cause of low prototype-to-production translation — passing outcomes can mask flawed trajectories that will break on slight input variation. Google Vertex AI provides trajectory_exact_match, trajectory_precision, and trajectory_recall primitives that pair directly with task success rate for production agent monitoring.
- 2026-03-14 / AGENTSGalileo Agent Control: Apache 2.0 Open Source Control Plane for Universal Agent Governance