Large Language Models (LLMs), like ChatGPT, have taken the Internet by storm. People are amazed at how these models can generate language with the snapshot of general world knowledge they have accumulated. However, in many real-world use cases a snapshot of general world knowledge is not enough. How do you equip them with the relevant tools to solve a task - like SQL, a CRM or web search? Or for a task, how do you control which tool is used or which knowledge base is referred to? Using LLMs as so-called “Agents” allows you to use them as a decision maker in your application that react to all sorts of user requests. Given a user request, the agents can create an action plan.

In this talk, we will learn how to build agent-driven applications with Haystack. We will show how to build an agent and connect it to other tools or knowledge bases. We will illustrate the concept with practical use cases and examples. Each step will be accompanied by code examples. By the end of the talk, you will have seen these concepts applied in practice, and you will be able to build an agent-driven application for your own use case.