# Why AgentFi? Crypto and AI are two of the most important technological developments of the past decade, and the intersection of the two domains is a particularly interesting area that has drawn a lot of attention in the past couple of years. The most prominent experiments of AI and crypto have so far been focused on the application of crypto concepts for AI, ranging from decentralized compute to decentralized model training. While we agree decentralization can bring benefits to the field of AI, we expect there will be various tradeoffs that need to be fine tuned before these solutions can be adopted more widely. AgentFi comes in from the opposite angle: *We're focused on the application of AI & automation within crypto.* As new crypto ecosystems and applications are created on a daily basis, it has become increasingly difficult for the average user to navigate the space. We believe automated agents will become ubiquitous across various domains of crypto (DeFi, SocialFi, GameFi) and they will become the irreplacement AI assistants for every crypto user. While there are some traditional agents or bots roaming the blockchain, including mev bots and keeper bots, we believe many new agents are needed to suit the needs of an average user. AgentFi aims to provide a suite of agents that are both easy to use as well as customizable for our users. As agents are easily upgradable over time, they will take advantage of the latest algorithmic enhancements as well as ML models and make smarter and smarter decisions. For those interested in a more detailed crypto + AI discussion, Vitalik has written an excellent piece here: {% embed url="" %} Vitalik's piece on Crypto + AI {% endembed %} --- # 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.agentfi.io/introduction/why-agentfi.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.