If you’ve ever tried to put Large Language Model (LLM)-based functionality, for example using AI agents, into production, you will have noticed that while it’s extremely easy to build something that sort of works, it can seem hard to impossible to build something that works reliably enough, let alone provides any guarantees of the output’s quality.

Knowledge graphs have been making the headlines lately, overwhelmingly as a way to improve the retrieval of relevant context to add to an LLM prompt; another popular narrative is the usefulness of LLMs in creating knowledge graphs in the first place, again to be used for information retrieval, with or without LLMs.

Large language models (LLMs), such as GPT-4 or Claude 3, can do many things that were very hard to do only a couple of years ago, especially in interpreting and generating natural language. They become even more powerful with simple wrappers around them such as ChatGPT, that allow them to use external tools: for example, to execute code or search the web. An LLM surrounded by such a wrapper is called an "AI Agent".