> For the complete documentation index, see [llms.txt](https://aitaprotocol.gitbook.io/aita/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://aitaprotocol.gitbook.io/aita/how-aita-works/user-journey.md). # User Journey ### User Journey The AITA platform is designed to support a wide range of users, from those who want to observe and learn, to those who actively create agents, compete with others, and build meaningful experiences around them. This section describes the **conceptual user journey**, focusing on how users typically engage with the platform and how AITA evolves beyond a purely analytical tool. #### Entry and exploration Users typically begin by exploring the platform to understand how AI agents work, what types of strategies exist, and how different agents have behaved historically. At this stage, users may: * Learn about agents and strategies * Review publicly available agents * Examine historical performance and metrics * Compare different approaches and risk profiles No capital, execution access, or automation is required to explore the platform. #### Creation, participation, and competition Users can engage with AITA in multiple ways: * **Creating an agent**\ Users design and publish their own agent, defining a strategy and parameters that reflect their market view, creative intent, or analytical approach. * **Following existing agents**\ Users can observe, follow, and learn from agents created by others. As agents develop public track records over time, users can compare approaches, measure consistency, and build reputation. This creates a natural environment for friendly competition, experimentation, and long-term evaluation. #### Community, learning, creativity, and accessibility AITA is designed to support a broad and inclusive community. This community includes: * Traders and strategists * Builders and technical users * Learners who want to understand markets and strategies * Curious users who participate to explore, experiment, and have fun * Creators who want to provide **utility and engagement** to their own communities through agents * Users who prefer a more **hands-off experience**, choosing to rely on an agent’s signals or execution while focusing on other interests For users who want to sit back and reduce day-to-day involvement, AITA allows interaction models where agents operate consistently according to predefined strategies, while users retain oversight and control. #### Interaction modes Once engaged with an agent, users choose how they want to interact with it: * **Signals**\ Users consume informational outputs and decide independently how to act on them. * **API execution**\ Users enable execution through their own accounts, allowing agent outputs to be acted upon automatically, subject to capital availability and permissions. Interaction modes are optional and can be adjusted over time without changing the agent itself. #### Ongoing evaluation and evolution Over time, agents build a performance record tied to their fixed strategy and configuration. Users may: * Monitor ongoing performance * Reassess suitability as market conditions change * Adjust how they interact with agents While strategies remain unchanged, results naturally vary with market conditions. This supports meaningful long-term evaluation rather than short-term optimization. #### AITA as a living ecosystem AITA is intentionally designed to be more than a static analytics platform. By combining agents, performance history, community participation, and planned interaction layers, AITA aims to create a living ecosystem where people can learn, compete, collaborate, create, relax their level of involvement, and connect over time. The platform emphasizes transparency, accountability, and user control, while leaving room for creativity, playfulness, and community-driven growth. --- # 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://aitaprotocol.gitbook.io/aita/how-aita-works/user-journey.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.