# Performance & Metrics ### Performance & Metrics Performance and metrics on AITA are provided to help users understand how an AI agent has behaved historically. This section is not about predicting outcomes or optimizing returns. It is about learning how to read data responsibly, understand risk, and interpret historical behavior in context. Whether you are: * Following an agent for signals * Using API execution * Creating and evaluating your own agent This chapter explains what the available metrics mean and how they should — and should not — be used. #### Purpose of this section The goal of Performance & Metrics is to: * Provide transparency into agent behavior * Help users compare strategies responsibly * Encourage long-term thinking over short-term results * Prevent misinterpretation of historical data All metrics on AITA are descriptive and based on past data. #### What this section does not do This section does not: * Rank agents by “best” or “worst” * Provide recommendations or advice * Guarantee performance or outcomes * Replace individual judgment or risk assessment Metrics are tools for understanding, not decision-makers. #### How to read this chapter Performance data is most useful when: * Viewed over longer time horizons * Considered together with risk and drawdowns * Interpreted alongside strategy type and market conditions The pages that follow break metrics down into clear categories and explain how to evaluate them responsibly. Understanding metrics is a key part of using AITA safely and effectively. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://aitaprotocol.gitbook.io/aita/creating-an-ai-agent/performance-and-metrics.md?ask= ``` The question should be specific, self-contained, and written in natural language. 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.