How to Evaluate Tool-Calling Agents

In evaluating tool-calling agents, the introduction of Large Language Models (LLMs) to tools introduces potential points of failure, such as incorrect tool selection or improper tool invocation, necessitating distinct measurement and correction methods. Phoenix provides a framework to assess these issues through two prebuilt evaluators: tool selection and tool invocation, which function without labeled datasets by reasoning from conversational context. In a travel assistant demo, Phoenix's evaluation workflow identifies and iterates on failures, such as incorrect date usage and semantic interpretation issues, by customizing evaluators to align with specific domain requirements. This iterative process not only improves the assistant's performance but also calibrates evaluators to ensure they accurately reflect the intended tool-calling behavior, with the results highlighting the importance of adapting evaluation tools to meet specific use-case constraints.

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