Agentic metrics help you measure how well your AI agents perform complex, multi-step tasks—especially when those agents need to use tools, make decisions, or interact with external systems. These metrics and helpful for those for anyone building advanced AI assistants, workflow automation, or any system where the AI acts on behalf of a user. Use agentic metrics when you want to:
  • Track whether your agent is making meaningful progress toward its goals.
  • Detect and diagnose errors that occur when your agent uses tools or APIs.
  • Ensure your agent is choosing the best tools or actions for each situation.
Below is a quick reference table of all agentic performance metrics:
NameDescriptionWhen to UseExample Use Case
Tool ErrorDetects errors or failures during the execution of tools.When implementing AI agents that use tools and want to track error rates.A coding assistant that uses external APIs to run code and must handle and report execution errors appropriately.
Tool Selection QualityEvaluates whether the agent selected the most appropriate tools for the task.When optimizing agent systems for effective tool usage.A data analysis agent that must choose the right visualization or statistical method based on the data type and user question.
Action AdvancementMeasures how effectively each action advances toward the goal.When assessing whether an agent is making meaningful progress in multi-step tasks.A travel planning agent that needs to book flights, hotels, and activities in the correct sequence.
Action CompletionDetermines whether the agent successfully accomplished all of the user’s goals.To assess whether an agent completed the desired goal.A coding agent that is seeking to close engineering tickets.

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