# Problem Statement AI technology has advanced, but current AI agents still face major challenges. They struggle to work independently, integrate with different digital systems, and learn over time. To unlock their full potential, AI agents must become more autonomous, adaptable, and connected across Web2 and Web3 environments. ### Key Challenges 1. **Lack of Human-like Adaptive, Self-Driven Intelligence** Unlike humans, who possess lifelong goals and the innate ability to learn, evolve, and execute complex tasks independently, current AI agents are limited to simple query processing and predefined workflows. Humans can intuitively break down objectives, self-learn, and adapt strategies while effectively collaborating with cross-functional teams to achieve intricate goals. This disparity results in low adoption rates for existing AI products, which fail to meet the dynamic and multifaceted demands of modern tasks—a critical gap that WORLD3's AI Agents are engineered to fill. 2. **Poor Web2 & Web3 Integration** AI agents have trouble working across traditional (Web2) and decentralized (Web3) applications. Main problems include: * **Not optimized for blockchain:** AI cannot easily use smart contracts and decentralized applications. * **Data access issues:** AI struggles to collect and process information from different sources. * **Security challenges:** AI lacks built-in support for decentralized identity and secure access. 3. **No Long-Term Learning** Most AI systems cannot learn and improve over time. Their main limitations are: * **Short memory:** AI forgets past interactions and repeats unnecessary steps. * **Limited adaptability:** AI cannot update its knowledge in real-time. --- # 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://docs.world3.ai/world3/readme-1/problem-statement.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.