AI Coding Agents
AI coding agents are AI tools that can plan and carry out software development tasks, such as writing, editing, testing, and explaining code, by working directly with a codebase and development tools.
Earlier AI coding assistants mostly suggested the next line of code or answered questions in a chat window. Coding agents go further. Given a task, they read the relevant files, propose a plan, make changes across several files, run commands and tests, and revise their work based on the results. Examples include tools such as Claude Code, Cursor, and GitHub Copilot. Developers use them in an editor, in a terminal, or as background tasks that return proposed changes for review.
A common misconception is that coding agents replace engineering judgment. They work best when a person defines the task clearly, sets acceptance criteria, and reviews the output before it is merged. Without that discipline, a team can quickly produce code that is hard to maintain, which adds technical debt. Teams adopting coding agents usually set standards for security, testing, and review, including limits on the code, data, and systems the agents can reach.
Key parts
Clear tasks. Well-scoped requests with acceptance criteria the output can be checked against.
Codebase context. Access to the files, conventions, and documentation the agent needs.
Tool use. Running tests, builds, and commands to check its own work.
Human review. Engineers review and approve changes before they are merged.
Guardrails. Limits on permissions, secrets, and access to production systems.
