Agentic coding
Agentic coding is using an AI tool that plans and executes a whole task in your codebase rather than completing one line at a time. The agent reads files, makes edits across several of them, runs commands, checks the result, and iterates until the task is done or it gets stuck.
The distinction that matters is who holds the loop. With autocomplete, you decide every step and the model fills in the next few tokens. With an agentic tool, you describe an outcome and the model decides the steps: which files to read, what to change, what command proves it worked.
That shift changes what a good prompt looks like. Autocomplete needs no prompt at all. An agent needs scope, constraints, and a way to check its own work, because it will make dozens of decisions you never see.
What this means in practice
Cursor's Agent mode, Claude Code, and Windsurf's Cascade (now Devin Local) are all agentic. GitHub Copilot's inline completion is not, though its agent mode is. The practical test: can the tool run your test suite and fix what it broke without you asking again? If yes, prompt it like a colleague. If no, prompt it like autocomplete.
Prompts for the tools this applies to
Related terms
- Coding agent
A coding agent is an AI system that carries out programming tasks end to end: reading a codebase, editing multiple files, running commands, and reacting to the output.
- Agent loop
The agent loop is the repeating cycle an AI agent runs: decide what to do next, take an action such as reading a file or running a command, observe the result, and repeat.
- Agent mode
Agent mode is the setting in an AI coding tool where the model can act rather than only answer: creating and editing files, running terminal commands, and iterating on the result.
- Autonomous coding agent
An autonomous coding agent completes a described task without step-by-step supervision: it plans, edits, runs, and verifies on its own, reporting back when done.