> For the complete documentation index, see [llms.txt](https://cattoverse-1.gitbook.io/cattoverse/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://cattoverse-1.gitbook.io/cattoverse/introduction/agent-system-architecture.md). # Agent System Architecture Catto is built on a multi-layer agent architecture. Each layer plays a specific role on the path from market state to user-controlled automated outcomes. #### **👁️ Perception Layer** Continuously ingests and scores signals from on-chain data, wallet tracking, social sentiment, market structure, protocol metadata, and news catalysts, ranked by relevance against your portfolio context. #### **🧠 Reasoning Layer** Interprets the signal environment to generate actionable opportunity assessments, risk condition evaluations, strategy-fit scoring, and recommended execution actions with confidence levels. This layer is the intelligence core of the agent, deciding what matters, what to surface, and what to act on. #### **⚡ Execution Layer** Translates decisions into blockchain-correct transaction objects, including asset resolution, route construction, fee estimation, approval management, payload assembly, and confirmation tracking. Operates only within your authorized parameters and cannot self-expand its permission boundaries. #### **🎚️ Autonomy Control Layer** You control how much the agent can act independently: 🔔 Notify only, Catto alerts you, you execute manually 📋 Prepare and confirm, Catto prepares the transaction, you sign to authorize 🤖 Conditional auto-execute, Catto executes within pre-authorized strategy rules ⏰ Scheduled execution, Catto runs defined tasks on a time-based schedule At no autonomy level does Catto hold private keys. All execution flows through wallet-native signing. --- # 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 dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://cattoverse-1.gitbook.io/cattoverse/introduction/agent-system-architecture.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.