Agent Evaluation

Learn how to rigorously evaluate agentic systems using metrics like task completion, trajectory efficiency, tool use correctness, and safety. Implement trajectory-based evaluation with LLM judges and build automated regression test harnesses for continuous improvement.

intermediate2-3 days4 steps

The play

Define Evaluation Metrics
Clearly define the metrics you'll use to evaluate your agent. Consider task completion rate, trajectory efficiency (e.g., steps to completion), tool use correctness (e.g., successful API calls), and safety violations (e.g., harmful outputs).
Implement Trajectory-Based Evaluation with LLM Judge
Use an LLM to judge the agent's trajectory. Provide the LLM with the task description, the agent's actions, and the environment's responses. Prompt the LLM to assess the trajectory based on your defined metrics. Consider using a structured output format (e.g., JSON) for easier parsing.
Build an Automated Regression Test Harness
Create a system that automatically runs your agent through a suite of predefined test cases. This harness should execute the agent, collect the trajectory data, evaluate the trajectory using your LLM judge (or other evaluation methods), and report the results. This allows you to track performance changes as you iterate on your agent.
Design a Leaderboard for Agent Comparison
Create a leaderboard to track the performance of different agent versions or different agents altogether. The leaderboard should display the key metrics you're tracking (task completion, efficiency, etc.) and allow you to easily compare performance across different agents. Consider using a weighted scoring system to combine multiple metrics into a single overall score.

Starter code

Start by defining a simple task and manually evaluating a few agent trajectories. This will help you refine your evaluation metrics and LLM prompts before automating the process.

Source

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