From Chatbot to Agent: How AI Coding Tools Take Action
A chatbot answers. An agent does. Here's the loop that turns a language model into a tool that can read files, run commands, and edit your codebase — and how to set one up.
A plain chatbot can only produce text. An agent wraps that same model in a loop and gives it tools — so instead of describing a fix, it can open the file, make the change, run the tests, and read the results.
The agent loop
- Observe — the model reads the task and the current state.
- Decide — it chooses a tool to call, or answers directly.
- Act — the tool runs (read a file, run a command, call an API).
- Repeat — the result feeds back in, and the loop continues until done.
Where tools come from
Tools are exposed to the model through a standard interface. The Model Context Protocol (MCP) has become a common way to plug capabilities — GitHub, a filesystem, a database — into any agent that speaks it. Add an MCP server and every compatible agent can suddenly use it.
A sane starting setup
You don't need every tool at once. A filesystem server for local files, a GitHub server for repo context, and a couple of good rules for code review and testing will take you a long way. Browse the catalog and copy what you need.