An AI that doesn't just suggest code - it writes, runs, and fixes it autonomously.
Imagine the difference between asking a friend for driving directions versus hiring a driver. A chatbot gives you directions - it tells you what code to write. A coding agent is the driver - it actually writes the code, runs it, checks if it works, and fixes problems on its own.
An AI coding agent is software powered by a large language model that can take autonomous actions on your codebase. It reads your files, understands the context, writes or edits code, executes commands, runs tests, and iterates based on the results. Tools like Claude Code, Cursor, Windsurf, and GitHub Copilot agent mode are all examples.
The key word is autonomous. A coding agent doesn't just predict the next line of code - it operates in a loop, taking actions and observing results until the task is done. It might create a file, realize it forgot an import, fix the import, run the tests, see a failure, and adjust its approach - all without you intervening.
Coding agents are the foundation that agentic engineering is built on. Without agents, you're just using AI as a fancy autocomplete. With agents, you can delegate entire tasks - "add pagination to this API endpoint" or "write tests for this module" - and get back working code.
But agents aren't magic. They're powerful tools that need direction. The quality of what an agent produces depends heavily on the context you give it: clear specs, good scaffolding, and robust guardrails. Agentic engineering is the discipline of providing that direction effectively.
The best results come when you treat agents like capable but context-limited junior developers. Give them clear instructions, set up guardrails, and always review their work.