AI coding tools have moved through three stages in just a few years. Most developers now use at least one.
Stage 1: autocomplete
The first wave suggested the next few lines as you typed. GitHub Copilot made this mainstream. It’s still useful for boilerplate and repetitive code.
Stage 2: chat in the editor
Next came assistants you can talk to inside your editor: explain this function, write a test, why is this failing? Editors built around AI, such as Cursor, also understand your whole project, not just the open file.
Stage 3: coding agents
The latest tools are agents. Give them a task (“add pagination to the orders API”) and they plan the change, edit many files, run the tests and fix failures in a loop. Agents now ship as terminal tools (for example Claude Code, OpenAI Codex and Gemini CLI), as modes inside editors and IDEs, and as cloud agents that open pull requests for review. (agentic AI 是什么？.)
Many of these tools can connect to your issue tracker, docs and databases through MCP.
Getting good results
- Be specific. Describe the goal, the constraints and how to check success (“all tests pass, no new dependencies”).
- Keep tasks small. One feature or fix per request is easier to review.
- Let it run the tests. Agents improve a lot when they can see failures.
- Review every change. Read the diff like a colleague’s pull request.
- Protect secrets. Keep API keys out of prompts and limit what the agent can access.
Free and open-source options
Not ready to pay? VS Code and VSCodium are free editors, and many AI extensions have free tiers. Developers who want self-hosted options can pair open-weight models with open-source assistants.
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常见问题
Will AI coding agents replace developers?
They change the job more than they replace it. Agents are good at well-defined tasks, boilerplate and refactors; people still decide what to build, design systems, review changes and take responsibility for what ships.
