> For the complete documentation index, see [llms.txt](https://whitepaper.nrnagents.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://whitepaper.nrnagents.ai/introduction/agents-the-next-big-theme-in-ai/what-are-ai-agents.md). # What are AI agents? AI agents are autonomous systems capable of performing tasks and making decisions on behalf of humans, often without needing constant human oversight. AI agents can leverage long-term memory, learn from repeated interactions, and handle a range of tasks autonomously, moving beyond chatbots or narrowly defined algorithms. In the coming years, the narrative will shift from text-based interaction models to agents capable of true autonomy across virtual and physical environments, fundamentally altering how industries like gaming, robotics, e-commerce, and enterprise software function. ## **Why is this so compelling?** AI Agents represent a new form of labor—one that is vastly more productive in the domains they automate compared to humans. In gaming, for example, AI Agents can autonomously fill multiplayer lobbies, ensuring players always have more engaging and dynamic gaming experiences. In robotics, physical agents trained in simulation environments can transfer their learned behaviors into the real world, autonomously navigating complex environments, manipulating physical objects, and performing intricate tasks traditionally requiring human dexterity and judgment. This capability of bridging virtual training with real-world execution accelerates the development of autonomous systems and unlocks immense value by not only streamlining operations but also creating opportunities for companies and individuals to monetize these agents. The productivity gains from automating workflows with AI Agents, both virtually and physically, are far-reaching, and the potential for economic value creation is massive. --- # 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://whitepaper.nrnagents.ai/introduction/agents-the-next-big-theme-in-ai/what-are-ai-agents.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.