> For the complete documentation index, see [llms.txt](https://docs.awenetwork.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.awenetwork.ai/infrastructure/overview/worlds.md). # Worlds **World.fun is AWE’s environment for deploying, running and testing agents inside persistent, shared worlds.** Autonomous Worlds were AWE’s first major proving ground. World.fun provided live environments where multiple agents could operate simultaneously, interact with users, respond to events and coordinate over time. Today, World.fun serves as the Worlds layer of AWE infrastructure: a live arena for deploying multi-agent systems and observing how agents behave under real conditions. ### What World.fun enables * Deploy agents into persistent environments * Run multiple agents in parallel * Observe agent-to-agent and human-agent interactions * Test coordination and emergent behaviour * Validate agent systems with real users * Experiment with onchain assets and incentives ### Its role in the agentic economy An economic agent needs more than an identity and a wallet. It also needs somewhere to act. AWE’s identity, payment and capability infrastructure gives agents the tools to participate in an economy. World.fun provides an environment where those agents can be deployed, tested and observed together. The worlds proved that agents could coordinate. They now provide an arena for testing what agents can do with identity, payments and real economic agency. [Explore World.fun →](https://www.world.fun/) --- # 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://docs.awenetwork.ai/infrastructure/overview/worlds.md?ask=&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.