What is agentic coding?
Agentic coding means using AI agents that can plan, edit files, run tests, and fix errors autonomously instead of just suggesting the next line.
The short version
Agentic coding is a step beyond AI autocomplete. Instead of only suggesting the next line or completing a function, an agentic coding tool receives a goal, plans the steps, reads your codebase, edits multiple files, runs commands and tests, observes the results, and iterates until the task is done or it needs your input.
How it differs from copilot-style autocomplete
A copilot suggests code as you type. An agent acts on a task. Claude Code, Cursor's agent mode, Devin, and OpenHands are agentic — they can open files, run shell commands, execute test suites, read error output, and revise their own work. The human shifts from typing every line to reviewing and directing the agent's output.
What agentic coding is good at
Agentic coding shines for multi-file refactors, bug investigation across a codebase, writing and running tests, scaffolding new features, and repetitive changes that touch many files. It is especially strong when the task has a clear success signal, like tests passing or a build succeeding.
Where agentic coding struggles
Agents can drift from the goal, make incorrect assumptions about architecture, over-edit files, or get stuck in loops when errors are unclear. They need clear instructions, good context, permission boundaries, and human review before changes ship to production.
How to use agentic coding well
Give the agent a specific, testable goal. Let it work in a branch or sandbox. Review the diff before merging. Use it for tasks where you can verify the result — tests, builds, lint checks — rather than open-ended design decisions that need human judgment.