Evaluating Deep Agents: Our Learnings

Evaluating complex "Deep Agents" requires more than just checking the final answer; it demands custom tests for each scenario to verify the specific sequence of tools used and changes made to the agent's state. You can efficiently test an agent's decision-making by running it for just a single step, which is perfect for confirming it chooses the right tool without running a full, costly sequence. For a complete picture, running full or even multi-turn evaluations allows you to assess the entire end-to-end process, from the agent's action trajectory to the quality of its final output after a realistic user interaction. A critical and often overlooked aspect is the need for a clean, isolated environment for each test run to ensure the results are reproducible and reliable.