> For the complete documentation index, see [llms.txt](https://docs.amesa.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.amesa.com/train-agents/analyze-agent-behavior.md). # Analyze Agent System Behavior Analyzing agent system behavior is a key part of using AMESA. This capability is key for: * Building agent systems that beat performance benchmarks * Monitoring the performance of your agent systems and simulators in real-time * Analyzing your agent systems and simulators to understand what went wrong in case of problems * Creating dashboards and reports to share with your team or customers AMESA offers multiple options for analyzing agent system behavior. For high-level summaries, you can use the benchmark reporting features in the UI. For more in-depth information, you can use the Historian and raw benchmark data in the SDK, which creates a database of all the information gathered during training and deployment. You can also design an LLM module in your agent to provide customizable communications capacity using natural language. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.amesa.com/train-agents/analyze-agent-behavior.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.