# 1. Executive Summary Omniminds, launched in May 2025, is an open-source platform focused on the development of Computer Use Agents (CUAs). It aims to build the foundation for powerful computer-use agents through crowdsourced action datasets, open-source tools, and agent models. From moving the cursor to filling forms, navigating software, or even completing tasks, these agent models learn directly from users through real-time demonstrations. We are creating an approach intended to allow automation of tasks that are not typically accessible via traditional APIs. With our ecosystem and infrastructure, anyone can teach, improve, deploy, and even monetize these autonomous agents. The idea is to create agents that can operate on any digital interface like a human would. Digital systems are becoming complex and fragmented, thus increasing the need for automation that requires few integrations or engineering overhead. Omniminds is proposing a solution where agents learn how humans work and adapt to a similar working environment through demonstration. We aren’t just creating a framework for teaching agents, but developing a decentralized agentic economy, where anyone can get rewarded for training and deploying agents. The economy is powered by our very own native token, $OMNIS. As the ecosystem grows, we envision a future where machine-to-machine interactions have human intelligence. Not only will this enable productivity and autonomy, but deliver real value. --- # 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://omniminds-ai.gitbook.io/whitepaper/1.-executive-summary.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.