The first time it worked, it felt like magic.
I typed half a sentence, and the AI filled in the rest, tests, edge cases, even doc comments. Like a dream you didn’t know you had until it showed up in your IDE.
But two weeks in, the cracks appeared.
The AI rewrote a query using a non-existent ORM method. It silently introduced a regression in a caching layer. It suggested mocking the wrong dependency, and no one caught it, because the PR still “looked fine.” What started as magic began to feel more like auto-pilot without a destination.
The truth is: coding with AI isn’t about clever completions. It’s about collaboration. And collaboration needs rituals.
We learned the hard way. But eventually, our team built a set of lightweight rituals that made the difference between “cool toy” and “useful partner.”
1. We named its roles.
Sometimes it’s a generator (first draft). Sometimes an explainer (why does this regex work?). Sometimes a refactorer, or a test-drafter, or a data-plumber turning JSON soup into typed models.
By naming the mode, we set expectations. “Hey, use the bot to sketch this, but don’t trust the details.” That shift, from passive autocomplete to active pairing, changed how we approached the tool.
2. We built a prompt library.
Not templates. Phrases that worked. Like “Refactor for readability without changing behavior.” Or “Generate test cases that fail under these conditions.” We saved them, iterated on them, shared them in the README.
The AI got better because we got better at speaking its language.
3. Every AI-generated diff gets flagged.
Whether it’s a commit prefix ([ai-gen]) or a comment in the PR, we call it out. Why? Because those are the ones that need different eyes, not just logic review, but intent review. Did the AI understand the business rule? Or just pattern-match its way into a confident lie?
4. We defined red-flag phrases.
If the AI says “likely,” “should,” or “in most cases,” we pause. Those words mean “I’m guessing.” And guessing doesn’t scale in production. These phrases became stop signs, reminders to verify.
5. We set stop conditions.
When do we not use the AI? When touching auth logic. When the change requires coordination with another team. When test coverage is below threshold. It’s not about fear, it’s about knowing when the cost of a wrong guess is too high.
6. We track different metrics.
Not just velocity. We look at PR cycle time. Escaped defects. Test coverage drift, not just percentages, but how well tests keep pace with complexity. If AI makes you code faster but test less thoroughly, you’re building a house on sand.
7. We reflect weekly.
One win. One failure. One surprise. That’s the ritual. Shared in Slack or a stand-up. Not because we love process, but because the tool is evolving, and so are we. What worked last week might backfire tomorrow.
AI coding agents can be brilliant.
But they’re not teammates until you treat them like teammates, with roles, rituals, feedback, and boundaries.
The magic isn’t in what they can generate.
It’s in how we learn to work with what they almost get right.