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name: agent-evaluation description: methodologies for assessing agent capabilities, tool-use trajectories, and final response quality. Use this to implement automated testing and human-in-the-loop validation for agents.

Agent Evaluation Frameworks

Goal

Implement a multi-layered evaluation strategy that assesses not just the final answer, but the reasoning process (trajectory) and core capabilities of the agent.

1. Evaluating Trajectory (The "How")

• Definition: Assessing the sequence of steps (thoughts, tool calls, observations) the agent took to reach a conclusion.
• Metrics:
  • Exact Match: The agent's path perfectly mirrors the ideal reference trajectory (rigid).
  • In-Order Match: The agent completed the core steps in the correct sequence, ignoring harmless extra steps (flexible).
  • Any-Order Match: The agent performed all necessary actions, regardless of sequence.
  • Tool Precision/Recall: Did the agent call relevant tools and avoid irrelevant ones?.

2. Evaluating Final Response (The "What")

• Definition: Assessing the quality, relevance, and correctness of the final output provided to the user.
• Mechanism:
  • Golden Datasets: Compare output against manually curated "correct" answers.
  • Autoraters (LLM-as-a-Judge): Use a strong model to grade the response against specific criteria (e.g., "Is the tone helpful?", "Is the answer grounded in the context?").

3. Assessing Capabilities

• Benchmarks: Use standard benchmarks to test fundamental skills before deployment:
  • Tool Calling: Berkeley Function-Calling Leaderboard (BFCL) to test tool selection accuracy.
  • Planning: PlanBench to assess reasoning and multi-step logic.

4. Human-in-the-Loop (HITL)

• Purpose: Calibrate automated metrics and assess subjective qualities like "creativity" or "nuance" that machines miss.
• Methods:
  • Direct Assessment: Experts score performance on specific tasks.
  • A/B Testing: Compare the new agent version against the old one in production.