Start Right with Deepchecks: Agent Evaluation Out-of-the-Box

Evaluating LLM-based applications, particularly those using multi-step agentic workflows, poses significant challenges due to their complexity and non-deterministic nature, which can obscure blind spots and complicate debugging. By using Deepchecks for agent evaluation, developers can obtain immediate and actionable metrics, allowing for a more efficient analysis of plan efficiency, tool coverage, and other performance indicators. The article illustrates this through a travel planning agent case study, where the Deepchecks dashboard revealed deficiencies in tool coverage, indicating that the agent did not have access to all necessary tools, resulting in hallucinated outputs. By swiftly diagnosing these issues, developers can decide whether to equip the agent with additional tools or adjust its task scope to align with its actual capabilities. The integration of Deepchecks requires minimal setup and provides visibility into potential agent failures, facilitating quicker troubleshooting and enhancing the reliability of agentic applications.