Six Key Metrics for AI Agent Evaluation

A practical guide to evaluating AI agents beyond simple task completion using the DeepEval open-source framework. Covers six agentic metrics split into two layers: full-trace metrics (PlanQualityMetric, PlanAdherenceMetric, TaskCompletionMetric, StepEfficiencyMetric) and component-level metrics (ToolCorrectnessMetric, ArgumentCorrectnessMetric). Also demonstrates how to use DeepEval's ConversationSimulator to auto-generate multi-turn test cases from scenario definitions, and how to apply conversational metrics like ConversationCompletenessMetric and TurnRelevancyMetric. Code examples show how to instrument agents with @observe decorators and run evaluations in a structured pipeline.