IntentAGI--First Action Layer for Autonomous Agent

IntentAGI Explainer Video

IntentAGI Explainer Video

In the wave of digitization, we stand at the forefront of technological innovation, where Web3 and Artificial Intelligence (AI) are reshaping the way we understand and interact with the world. With this progress comes a unique opportunity: to use Agent technology to solve long-standing issues while exploring endless future possibilities.

Against this backdrop, we are proud to introduce our project: IntentAGI, a pioneering initiative designed to define the first action layer for autonomous agents. Before delving deeper, it's essential to highlight the profound connection between the name IntentAGI and our project.

We've observed a critical fact overlooked by many in our industry: any AI agent that doesn't start from understanding a user's intent (Intent) to generate actions (Action) that meet task requirements is incomplete. This simple yet profound insight is the cornerstone of building a truly effective AI agent.

The agent framework involved in IntentAGI is born from this concept, representing a natural transition from intent to action, ensuring each action is designed to achieve the user's specific goals.We firmly believe that the birth of AGI will be a process of quantitative to qualitative change (scaling law).

Under the agent framework of IntentAGI, we propose a data-driven AI agent framework and model, where users' intentions and behaviors can be effectively evaluated and quantified. Through sufficient scenario experience, training, and computation, the emergence of AGI becomes a foreseeable future (1 billion token law). This methodology not only showcases technological progress but also our profound understanding of the AI development path.

Lastly, the reason we firmly believe Web3 is the essential path to achieving this goal is simple: the tasks and knowledge of the human world are fundamentally decentralized. Each person possesses unique knowledge and skills, completing various tasks in different environments. The background of this project is distinct from any previous AIGC (AI and Generative Content) models, such as LLM (Large Language Models), text-to-image generation, and those whose pre-training datasets predominantly come from the internet.

Therefore, an end-to-end "intent to action" dataset that encompasses this diversity can only be built through a decentralized approach.

Moreover, we will address the wealth distribution issue in the process of AGI's birth, calculating every effective action involved in the birth of AGI and enjoying the benefits it brings.