# Fundamentals ## What is an agent anyway? Why should I care? AI agents are systems that use advanced reasoning and integrated tools to achieve user-defined goals. Agents are superior to and distinct from large language models (ChatGPT, Claude etc.) in that they can independantly plan and execute tasks - without human intervention.

Example of Assistant Agent by Neil Stephenson

SAG3 is a research agent. It's goal is to provide users with detailed, verifiable research and analysis for a given crypto project or token. It does this by combining the advanced (slow-ish) reasoning capabilities of large language models with hyper-efficient coordination and automation tools, built in-house. With first-mover advantage and a live product (which is more than most can say) SAG3.ai is positioned to capture a large part of the market for AI-powered research. We offer a better product than others and at a cheaper price. {% hint style="info" %} NFA: this is not yet reflected in the valuation of the [SAG3 token](https://jup.ag/tokens/Gx5dX1pM5aCQn8wtXEmEHSUia3W57Jq7qdu7kKsHvirt). {% endhint %} Okay, but what's to stop copycats with bigger teams and limitless marketing budgets from building the same thing and taking SAG3's market share? ## The Data Moat In the first 30 days from launch, SAG3 analysed and stored over 1000 distinct crypto projects. Every project that gets analyzed is added to our database. By storing and retrieving existing analysis data, SAG3 can work smarter, not harder. Instead of running (and rerunning) slow and compute-intensive reasoning models 30 times per day, SAG3 can simply retrieve existing analysis data in an instant. From the user's perspective, this means you get all the benefits of premium tier, cutting-edge AI models without any of the typical trade-offs, such as gated access, inference time and cost. The data moat is so valuable in fact, that we decided to give SAG3 away for free (for now). --- # 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.sag3.ai/sag3-platform/markdown.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.