Guide
What Is an AI Coding Agent?
An AI coding agent is a tool that can inspect code, plan changes, edit files, run commands and report results. It differs from AI chat because it acts inside the development workflow. A good agent produces a reviewable diff instead of only an answer.
What is the working pattern for AI coding agent basics?
- Move
- Start with a bounded task
- Use this when
- Developers and buyers who need a clear definition before tool selection.
- Proof to save
- Issue, files, checks and owner are named
- Move
- Give the agent context
- Use this when
- The repo has patterns the agent must follow
- Proof to save
- Prompt cites files, errors and constraints
- Move
- Review the diff
- Use this when
- The task changes production code
- Proof to save
- Changed files, test output and risks are visible
| Move | Use this when | Proof to save |
|---|---|---|
| Start with a bounded task | Developers and buyers who need a clear definition before tool selection. | Issue, files, checks and owner are named |
| Give the agent context | The repo has patterns the agent must follow | Prompt cites files, errors and constraints |
| Review the diff | The task changes production code | Changed files, test output and risks are visible |
A good AI coding workflow is specific enough to review and small enough to recover.
Interactive diagram. Use Tab to move through hotspots or use the step controls when shown.
Use this loop when the change is larger than autocomplete and smaller than a full project rewrite.
Interactive diagram. Use Tab to move through hotspots or use the step controls when shown.
Open each stage to see what a reviewer should be able to inspect.
The public guide connects to lessons, recall and readiness checks inside Learn Cursor.
How should a team run AI coding agent basics?
- 1Pick one real backlog item with a clear owner and expected result.
- 2Add only the context the agent needs: files, failing output, constraints and done state.
- 3Ask for a plan before code when the task touches more than one file.
- 4Run checks that match the risk: unit test, typecheck, visual pass or review checklist.
- 5Capture the prompt, diff, result and reviewer note so the workflow can be repeated.
Task, context, constraints, done state and checks.
Open the diff, read changed files and rerun the check yourself.
The guide explains actions, tools, context, approvals and handoff evidence.
What should you keep after the run?
- The prompt or plan that shaped the work.
- The files changed and the reason each file changed.
- The command, screenshot or review note that proved the result.
- The rule, checklist or template you would reuse next time.
Frequently asked questions
Who is What Is an AI Coding Agent? for?
Developers and buyers who need a clear definition before tool selection.
What makes this page credible?
The guide explains actions, tools, context, approvals and handoff evidence.
What should I do next?
Start with one real repo task, capture the prompt and review the result before scaling the workflow.
Sources & last verified
- Cursor agent best practices
- Cursor Learn: working with agents
- Cursor Learn: context
- Cursor docs: prompting agents
Cursor ships frequently. Facts verified against primary sources on June 23, 2026.