An AI coding agent combines a model with repository access, search, shell commands, editors, build tools, tests, and version-control context. It can investigate a request, plan changes, edit files, run checks, and report evidence instead of producing only a code suggestion.
A coding agent is most reliable when the repository makes requirements and constraints discoverable. Clear architecture, concise project instructions, lint rules, tests, and continuous integration give the agent current executable evidence and reduce dependence on stale conversational memory.
ELI5
A coding agent is more than a chat box that suggests a few lines. It can look through a software project, use development tools, change files, and check whether its changes actually work.
For example, you can ask one to add a search box. It might find the relevant page, update the code, run the tests, open the result in a browser, and then explain what it changed and what it verified.
How is an AI coding agent different from code completion?
A coding agent can inspect a repository, use tools, make multi-file changes, run verification, and adjust its approach based on results.
What context does an AI coding agent need?
It needs the relevant source, project instructions, dependencies, architecture, current worktree state, tests, permissions, and acceptance criteria.
