A Toolbox With a Hundred Drawers
Picture a new worker at a hardware shop. On day one there are five drawers behind the counter: screws, nails, glue, tape, hooks. A customer asks for glue, and the worker opens the right drawer every time.
A year later the shop has a hundred drawers. Some are labelled almost the same: "wood screws", "metal screws", "screws for plasterboard". Now the worker sometimes opens the wrong one. Is it because there are a hundred drawers? Or because some of them look alike?
An AI agent picks from tools the way that worker picks from drawers. Teams keep adding tools, and many say the agent gets worse at picking as the list grows. Lesson 02 of this chapter drew that as a curve, and was honest that the curve was a pattern teams report, not a measurement. This lesson measures it with two small models, and asks which of the two causes does the damage. The answer surprised me, and it pointed at a third problem nobody draws: the software between the model and your code.
The Words You Need First
Please read this slide slowly if any word is new. Every slide after it uses these words.
Model. Here, a language model: an AI that reads text and writes text, like the one behind ChatGPT.
Agent. A program that lets a model take actions, not just write text.
Tool. One action the program can do for the model, like "get the status of an order". Each tool has a name, a one-line description, and the details it needs, like an order number.
Tool call. The model's reply when it chooses a tool: the tool's name and the details to pass in. The program then runs that tool.
Look-alike tools. Tools with close names and close jobs, like get_order_status and get_order_items. In this lesson, a group of five look-alikes is called a family.
Prompt. Everything the model reads before it replies. Every tool on offer is written into the prompt, so more tools means a longer prompt.
Token. A small piece of text a model reads or writes, roughly three quarters of a word.
JSON. A fixed way to write data so a program can read it. A tool call is written in JSON, for example {"name": "get_order_status", "arguments": {"order_id": "48213"}}.
Ollama. A free tool that runs models on your own machine. Programs talk to it through its chat API, a web address that takes a message and returns the model's reply, including any tool call.