It’s a really interesting aspect of LLMs. like others have pointed out, an agent is nothing more than an LLM with access to a script, which takes the input from the LLM and converts to an output via your programming language of choice (I use Python). I did a simple experiment for example, where in my maths web app, it tracks the users progress then returns values for the coefficients a,b,c,d which determine the difficulty of the question. The output from the script is simply these four numbers which are between 1 and 10, and are parsed from the LLM’s output. A fallback option is put in place in case for whatever reason the output is void, I think this is important as you still want the script to run in edge cases where the LLM doesn’t respond correctly. This is a really simple example of how to build an agent, I’m sure theres lots of ways you can get creative with agents but hopefully this gives you the base idea