Simon Willison’s Weblog

Agents (via) Chip Huyen's 8,000 word practical guide to building useful LLM-driven workflows that take advantage of tools.

Chip starts by providing a definition of "agents" to be used in the piece - in this case it's LLM systems that plan an approach and then run tools in a loop until a goal is achieved. I like how she ties it back to the classic Norvig "thermostat" model - where an agent is "anything that can perceive its environment and act upon that environment" - by classifying tools as read-only actions (sensors) and write actions (actuators).

There's a lot of great advice in this piece. The section on planning is particularly strong, showing a system prompt with embedded examples and offering these tips on improving the planning process:

• Write a better system prompt with more examples.
• Give better descriptions of the tools and their parameters so that the model understands them better.
• Rewrite the functions themselves to make them simpler, such as refactoring a complex function into two simpler functions.
• Use a stronger model. In general, stronger models are better at planning.

The article is adapted from Chip's brand new O'Reilly book AI Engineering. I think this is an excellent advertisement for the book itself.