# Omnichain Automation Functor’s architecture fundamentally upgrades the web3 ecosystem by shifting operations from manual, human-triggered execution to **autonomous**, **machine-driven** systems. \ \ This is powered by our core capabilities in **cross-chain execution logic** and real-time strategy triggers. By enabling programmable permissions and secure, self-custodial signers, Functor empowers AI agents to act independently and verifiably onchain. This unlocks a new design space for **agent-to-agent commerce** and coordination that can operate across **any chain** without direct human intervention. For developers and funds, this translates into powerful, real-time use cases: ### **Autonomous Traders** AI agents can be deployed to execute complex trading strategies 24/7. These agents can perform **agentic payments** and conduct trades across multiple protocols and chains, all while remaining fully **self-custodial**. This means the agent acts on your behalf without ever taking ownership of your assets, providing unparalleled security for automated systems. ### **Dynamic Capital Allocation** Capital can be automatically reallocated between different strategies, chains, or markets based on predefined triggers or real-time data inputs. *For example, an agent could move assets to a higher-yield farm on a different chain the moment it becomes available.* ### **AI-Native Financial Markets** Functor’s infrastructure supports the creation of entirely new financial markets designed for the speed and scale of an **agentic economy**, all while maintaining **self-custody** and **onchain security**. --- # 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://functor.gitbook.io/functor-network-docs/use-cases/omnichain-automation.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.