# How EDGM Thinks The same structure that routes crypto and stock queries through agents and tools can also be viewed as a layered thought process. [EDGM](/edgen-litepaper/technical-principles/edgm-the-commander-model.md) assigns tasks, but goes a step further too: it orchestrates them, shaping raw inputs into a single line of reasoning. [EDGM](/edgen-litepaper/technical-principles/edgm-the-commander-model.md) is designed to think the way an organized research desk would, but at machine scale. Instead of one giant model guessing at answers, it acts as a commander that coordinates layers of analysis.
It begins with a **personal engine**. This engine interprets the user’s intent, adapts to their portfolio or query, and decides which type of analysis to activate. It then calls on **specialist agents**. Each agent is narrow by design: fundamentals, technicals, momentum, macro, or sector. Their focus makes outputs precise. Together, they cover the different angles needed to understand a market move. These agents rely on **market tools:** earnings reports, derivatives data, on-chain activity, macro releases. Outputs are collected, cleaned, and cross-checked against Edgen’s knowledge base. **Finally, the commander fuses everything into a single answer.** Instead of fragmented views from separate tools, the output is coherent, connected, and decision-ready. This layered process delivers three things professional traders demand: * **Coherence:** one answer that combines all relevant perspectives. * **Adaptability:** when conditions shift in one domain, agents in that domain adapt without breaking the system. * **Efficiency:** compute is allocated only where needed, making responses fast and cost-effective. The result is a system that thinks more like an experienced team of analysts than a generic AI, but runs continuously and at global scale. As we mentioned, [EDGM](/edgen-litepaper/technical-principles/edgm-the-commander-model.md) works like a head chef in a professional kitchen. It doesn’t cook everything itself. It directs specialists (one prepares proteins, another handles sauces, another manages timing) and brings them together into a single dish. The output is coherent and consistent, not a random mix of parts. --- # 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://gitbook.edgen.tech/edgen-litepaper/technical-principles/how-edgm-thinks.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.