> 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/applications/core-interaction-workflow.md). # Core Interaction Workflow The Autonomous Worlds Engine (AWE) enables seamless collaboration between Worlds, Agents, and Events through a structured, iterative process. Below is the neutral and universally applicable workflow:
* **World Initialization** * World Orchestration Module: Users configure the foundational parameters of a World, such as environmental rules, interaction protocols, and resource dynamics. * **Agent & Event Creation** * Agent Orchestration Module: Users design autonomous agents with roles and objectives tailored to the World’s purpose. Agents are programmed to act, adapt, and collaborate. * Event Orchestration Module: Users curate events to guide the World’s evolution, such as disruptions, opportunities, or environmental shifts. * **Event-Driven Agent Activation** * Event Orchestration Module triggers scenarios that alter the World’s state (e.g., a supply chain bottleneck). * Agent Orchestration Module dynamically generates agent responses, leveraging: * Agent Memory: Historical data (e.g., past delivery delays or successful rerouting strategies). * Activity Generator: Logic to synthesize actions (e.g., rerouting shipments or redistributing resources). * **Agent Collaboration & Adaptation** * Agents interact within the World, governed by their objectives and the Agent Orchestration Module’s frameworks. * The Event Monitor tracks outcomes (e.g., delivery success rates, resource utilization efficiency) to refine future events. * **Iterative Refinement** * Feedback loops between the Event Orchestration Module and Agent Orchestration Module drive continuous improvement. ## **Continuous Adaptation and Learning** The integration of these components ensures that Autonomous Worlds are not static but continuously evolve based on user input, agent interactions, and feedback. Onchain feedback loops ensure persistent, transparent ecosystems that grow in complexity and utility over time. --- # 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/applications/core-interaction-workflow.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.