What is an AI Agent
An AI agent is a system built around a large language model (LLM) that can use tools, observe results, and decide next actions in a loop. Unlike a simple chatbot that generates one response and stops, an agent works through multi-step tasks autonomously — reading files, running commands, fixing errors, and iterating until the job is done.
How it works
An agent runs in a loop:
- Observe — read the current state (user request, tool output, error messages)
- Reason — decide what action to take next based on everything in the context window
- Act — call a tool (search files, run a query, edit code)
- Observe again — read the tool's result and decide whether the task is complete
This loop continues until the agent decides the task is finished or it reaches a limit. Each iteration adds to the context — the agent sees the full history of what it tried, what worked, and what failed.
The quality of an agent depends on three things: the LLM's reasoning ability, the tools available to it, and how well the context is managed as the conversation grows.
Why it matters
Agents transform AI from a text generator into a task executor. A chatbot can tell you how to fix a bug. An agent can find the bug, write the fix, run the tests, and open a pull request. The difference is the tool-use loop — the ability to take action, see what happens, and adapt.
MCP is what makes agents practical at scale. Instead of hardcoding every tool an agent might need, MCP lets agents dynamically discover and use tools from any compatible server.
See How AI Agents Work for the full breakdown of the agent loop, error recovery, and multi-step reasoning.