> For the complete documentation index, see [llms.txt](https://aurexgold.gitbook.io/aurexgold-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://aurexgold.gitbook.io/aurexgold-docs/ai-agent-framework.md).

# AI Agent Framework

Aurex Gold operates six specialized AI agents, each responsible for a distinct area of market analysis. Together, they form a coordinated trading intelligence team.

| Agent    | Role                 | Function                                                                                                     |
| -------- | -------------------- | ------------------------------------------------------------------------------------------------------------ |
| Agent 01 | Market Analyst       | Continuously monitors price action, volatility, momentum, and market structure                               |
| Agent 02 | Macro Analyst        | Tracks Fed decisions, CPI, employment data, interest rates, USD, treasury yields, and global economic events |
| Agent 03 | News Intelligence    | Scans global headlines and identifies events that may directly influence gold prices                         |
| Agent 04 | Technical Strategist | Analyzes trends, patterns, indicators, breakouts, reversals, and S/R levels                                  |
| Agent 05 | Risk Manager         | Evaluates position sizing, stop-loss, take-profit, volatility, drawdown, and R/R ratios                      |
| Agent 06 | Strategy Agent       | Combines all conditions and user preferences to generate high-probability trading setups                     |

## Gold AI Score

A composite confidence score from 0 to 100, built from six weighted signal groups — **Technical, Momentum, Macro, USD, Volatility, and Sentiment**. This is an analytical confidence metric, not a probability of profit or a guarantee of returns.


---

# 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://aurexgold.gitbook.io/aurexgold-docs/ai-agent-framework.md?ask=<question>&goal=<user_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.