Harrison Chase

It's easy to build a prototype of an agent, but hard to put an agent in production - especially in an enterprise setting. In this section, will talk about three ingredients for building reliable agents in the enterprise.

A main issue with agents is that they are not reliable enough. It's easy to build a prototype of agents, but it's still a lot of engineering work to get them to perform reliably. Infrastructure (and tooling) can help make this easier - but thinking about and understanding your application's "cognitive architecture" will always be necessary.

How can companies best build useful and differentiated applications on top of language models? Many of the products and companies built do this by providing the relevant context to LLMs and asking it to reason appropriately.

# What We'll Build In this workshop, we will first go over what OpenAI function calling is. We will then go over two of the big use cases for it within LangChain: structured data extraction and tool usage (for agents) This will include: - An overview of function calling and all the different parameters - How to use function calling within LangChain (specifically, within **LangChain Expression...