The Agent Loop & Tools
At the heart of an agent is a loop: think → call a tool → observe the result → decide the next step. The tools you give it define what it can do.
How the Loop Turns
The agent does not answer in one shot. It runs the same cycle over and over until the job is finished.
- The model plans its next move
- It calls a tool to act in the world
- It reads the tool's output and updates its thinking
- It repeats until the task is done — or it gives up
Choosing the Tools
Give the agent exactly the tools the task needs — an API call, a database query, code execution. Too few and it gets stuck; too many and it wanders.
The Tool Surface
Tools reach the agent through a few common shapes. The names matter less than the idea: a clear, safe boundary between the model and the world.
- MCP — a standard way to expose tools and data to a model
- CLI / shell — running commands the way a person would in a terminal
- Sandboxed code execution — the agent writes and runs code in a walled-off space
Observe and Stop
An agent needs a clear success test and a stop condition. Without them it either loops forever, burning cost, or declares victory too early and ships the wrong result.
Build It
How to implement: for one agent feature, list the tools it needs and its stop condition — “done when the task is stored” or “done when the answer is produced.”
- Weekly AI Tasks tracker — the agent's tools are “add task,” “query tasks,” and “summarize”; the stop condition is a stored task or a delivered summary.
- Personal brand site — the tools are “read source file” and “write section”; stop when every section has grounded copy.
What you learned
An agent is a loop — think, act with a tool, observe, repeat. Give it exactly the tools the task needs, expose them safely, and always define how it knows it is done.