How AI Agents Use Tools — The Loop That Powers Everything

Every AI agent you have ever heard of — coding assistants, research agents, customer support bots that actually look things up — runs on one mechanism. Not a family of mechanisms. One. Once you can picture it, agents stop being mysterious.

The loop

You give the model a goal and a list of tools it may use. Then four steps repeat until the model decides it's finished:

• 1. The model responds. It either answers (done) or says "call this tool with these inputs."
• 2. Your code runs the tool. The model cannot execute anything itself — it emits a request, and your program decides whether and how to fulfil it.
• 3. You send the result back as part of the conversation.
• 4. The model reads the result and decides again — call another tool, or answer.

That's the entire engine. An agent that searches your docs, runs a test, reads the failure, patches the code, and re-runs the test is executing this loop five times with different tools.

What a tool actually is

A tool is just a function you describe to the model in structured form: a name, a plain-English description of what it does, and a schema listing its inputs. Something like: name search_orders, description "Find customer orders by email address", input email (string, required).

The model never sees your implementation. It only sees that description — which is exactly why the description matters so much. It is the entire basis on which the model decides whether this tool is the right one for the situation in front of it.

Who runs what — the part people get wrong

The most common misconception is that the model "has access" to your database or your email. It does not. It produces a request — a structured message saying it would like to call send_email with these arguments. Your code receives that request and decides what happens next.

This is the security boundary, and it is entirely yours to enforce. You can validate the arguments, require human approval before anything irreversible, rate-limit, log, or simply refuse and hand back an error — the model reads that error and adapts. An agent can only ever do what its tools permit, which is why the tools you expose are the real definition of what your agent can do.

Designing tools the model will actually use well

Most agent problems are tool-design problems wearing a disguise. A few things reliably help:

• Say when to use it, not just what it does. "Search the order database" describes the function. "Call this whenever the user asks about an order status, refund, or delivery date" tells the model when to reach for it — and measurably improves how often it does.
• Keep the set small and distinct. Two tools with overlapping purposes make the choice ambiguous. Fewer, clearly separated tools beat a large fuzzy menu.
• Promote the risky things to their own tool. A general "run any command" tool is powerful but gives your code no way to distinguish reading a file from deleting a database. A dedicated delete_record tool is something you can gate behind confirmation.
• Return useful failures. "Error" teaches the model nothing. "No customer found with that email; try searching by order ID" lets it recover on its own.