Coding with AI agents without breaking your project
AI agents no longer just autocomplete lines: they read your codebase, edit files, run commands and execute tests. Used well, they multiply your output. Used carelessly, they fill your code with bugs and security risks.
Key takeaways
- An agent plans and executes multi-step tasks in your project; it's not just autocomplete.
- The golden rule: Git, small tasks, tests and human review of every change.
- Never give an agent production credentials or permissions it doesn't need.
What is an AI coding agent?
A classic code assistant suggests the next line. An agent takes a goal ("add a contact form with validation and tests"), explores the project, proposes a plan, edits several files, runs terminal commands and fixes its own mistakes until it's done.
That changes the developer's role: you go from writing every line to defining the task well, supervising and reviewing. It's a new skill, and people who master it ship faster without sacrificing quality.
The most popular tools
| Tool | Type | Best for |
|---|---|---|
| Claude Code (Anthropic) | Agent in the terminal, IDE and web | Long tasks in large codebases, refactors, migrations |
| Cursor | Code editor with built-in AI | Day-to-day development with autocomplete and an in-editor agent mode |
| GitHub Copilot | IDE extension and agent on GitHub | Teams already on GitHub; tasks assigned from issues |
| Gemini CLI / Codex | Terminal agents | Google and OpenAI alternatives with similar workflows |
| Google Stitch | UI generator | UI design prototypes from text or sketches, exportable to code |
You don't need all of them. Pick one for code and, if you design interfaces, one for prototypes. What makes the difference isn't the tool but the method.
A workflow that doesn't break things
1. Git comes first
Always work on a new branch and make small commits. If the agent gets something wrong, you can roll back in seconds. Without version control, using an agent is a gamble.
2. Give it context in a file
Create an instructions file at the project root (CLAUDE.md, AGENTS.md or your editor's rules) with what a new developer would need to know:
# AGENTS.md
## Project
Laravel 11 + Livewire store. PHP 8.3, MySQL.
## Commands
- Tests: php artisan test
- Code style: ./vendor/bin/pint
## Rules
- Never modify migrations that already ran; create new ones.
- UI copy in U.S. English.
- Never read or edit the .env file.3. Ask for a plan before code
For any non-trivial task, ask for a plan first: which files it will touch and why. Fixing a plan takes a minute; fixing 400 lines headed the wrong way takes an afternoon.
4. Small, verifiable tasks
"Build the store" fails. "Add a phone field to the signup form, with U.S. format validation and a test" works. The more concrete the goal, the better the result.
5. Tests are the contract
Ask the agent to write or run tests and not to call the task done until they pass. If your project has no tests, that's a great first job for an agent.
6. Review every diff
Read the changes the way you'd review a new teammate's work: did it delete something it shouldn't have? Did it disable a validation to make a test pass? Did it add an unnecessary dependency? The agent is fast, not infallible.
Security: the risks nobody mentions
An agent with access to your terminal can do anything you can. Configure its permissions with the same care you'd use when giving a contractor access.
- Keep secrets out of reach. API keys and passwords belong in environment variables, never in the code or the chat. Use development credentials, not production ones.
- Least privilege. Check which commands it can run without asking. Destructive or deployment commands should require your approval.
- Prompt injection. If the agent reads web pages, issues or third-party files, that content may hide malicious instructions. Treat it as data, not orders.
- Hallucinated packages. Models sometimes suggest dependencies that don't exist, and attackers register those names with malicious code. Verify every new package on npm, Packagist or PyPI before installing it.
- Trusted connectors (MCP). MCP servers extend what an agent can do (databases, browsers, APIs). Install only ones from trusted sources and review what access they request.
Use cases that are worth it
- Migrations: moving a WordPress site to static HTML, or upgrading a major version of Laravel or React.
- Tests: generating a test suite for legacy code before changing it.
- Prototypes: generating screens with Stitch, validating them with the client and then implementing them with an agent.
- Maintenance: fixing accessibility warnings, improving technical SEO or updating dependencies and their breaking changes.
- Documentation: explaining a project nobody understands and documenting it for the next developer.
Frequently asked questions
Will AI agents replace developers?
No. They change the work: less time writing repetitive code and more time defining, reviewing and testing. Without someone who understands what's being built, mistakes pile up fast.
Can I use an agent if I don't know how to code?
For prototypes and internal tools, yes. For anything that handles payments, personal data or real customers, you need an experienced developer to review the code and security before it goes live.
Is it safe to give an AI access to my code?
It depends on the plan and the tool. Review the data policy and whether your code is used for training, never share passwords or production keys, and limit the agent's permissions to what it needs.
Conclusion
AI agents are the biggest productivity boost web development has seen in years, but they don't remove responsibility. Use Git, give context, ask for plans, require tests and review every change. With that method, one developer can deliver in days what used to take weeks.
