> For the complete documentation index, see [llms.txt](https://pclaw.gitbook.io/pclaw/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://pclaw.gitbook.io/pclaw/welcome-agent.md). # Welcome Agent pCLAW is an autonomous AI-powered crypto execution and social intelligence platform built as a **agent infrastructure**. It enables anyone to create, deploy, and run AI agents for **social automation and on-chain trading execution** without writing code. *** #### 🤖 AI Agent Network pCLAW operates a network of autonomous AI agents that run workflows across platforms like **Telegram and X (Twitter)**. Users can easily create agents through a visual interface to: * monitor trends * generate insights * engage with communities * automate social interactions in real time *** #### 🧩 GUI-Based Agent Infrastructure At its core, pCLAW provides a **no-code system** that allows users to: * build custom AI agents * define workflows and triggers * connect social signals to execution logic This turns complex automation into a simple, accessible experience. *** #### 🌐 Social & Crypto Discovery Layer The platform continuously analyzes: * on-chain data * social sentiment * trending narratives to surface early opportunities across the crypto ecosystem. *** #### ⚡ Autonomous Smart Trading on BSC pCLAW integrates AI-driven trading execution on BNB Chain, allowing agents to: * automatically enter and exit positions * manage trades based on signals * execute strategies in real time This creates a seamless bridge between **information → decision → execution**. --- # 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://pclaw.gitbook.io/pclaw/welcome-agent.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.