A transactional workflow for AI-assisted development

AI-assisted coding is no longer a curiosity. We’ve reached a level where models can generate strong implementation drafts quickly. The question I care about now is different: how do you turn that speed into production-ready outcomes without slipping into chaos, endless iteration or unverified code?

In practice, a framework that keeps AI useful in real engineering work is built on three connected ideas: a transactional model with atomic commits and rollback, Git as a context manager, and TDD 2.0 — where humans write tests and AI writes code.

Before talking about production workflows, I need to clarify how I see vibecoding. Vibecoding is an automation design instrument. It’s not an end‑to‑end system builder. It’s a set of capable helping hands for someone who already knows what they want to achieve.

This distinction matters. When you treat AI as a magical agent that “builds the whole thing,” you set yourself up for disappointment. But when you treat it as a fast executor inside a well‑defined process, everything changes. The more you practice vibecoding, the more you understand its boundaries and the more effective it becomes.

When you’re working with AI, the more atomic commits it delivers, the easier it is to guide and navigate it through the development process.

That’s the core of the transactional model. Instead of trying to fix a broken implementation through endless iteration, you commit frequently with atomic changes, and if validation fails, you rollback and restart. Each commit should be small, independent, and complete—like a feature flag you can toggle.

In this approach, rollback is not an exception. It’s part of the workflow. The cost stays minimal because changes are atomic. That’s what makes it practical: you can move forward in small verified steps, and you can reverse quickly when needed.

The concrete detail that supports this model: commit messages can include the prompt sequence used to generate the code and rollback instructions. This makes each change easier to understand, reproduce and undo.

Managing context and keeping context is one of the weak points of today’s coding agents. That’s why we need to be mindful about how we manage context for the agent.

My solution is to embed context within the commit history itself. Instead of relying on the AI’s memory across long sessions, the repository becomes the persistent place where context lives. Each commit links back to the reasoning behind that code: why it was accepted, what iterations it took and how to undo it if needed.

This creates a context trail that survives beyond any single AI session. When you start new work, you can reference previous commits and their context to guide the AI. The transactional model isn’t only about rollback, it also creates institutional memory that improves continuity as the codebase grows.

I would never allow AI to write the test. I would do it by myself. Still, it can write the code.

This is how I describe TDD 2.0. Humans write tests and AI writes the implementation code to make those tests pass. Tests become executable requirements and prompts. AI can iterate on implementation until tests pass but it shouldn’t be the author of the validation criteria.

The tests represent domain knowledge, business requirements and validation rules that humans must control. This creates a clear verification mechanism and helps avoid the biggest mistake: writing AI-assisted code without proper validation.

As AI becomes a mandatory skill for engineers, the teams that succeed will be the ones that adopt structured workflows. Not because AI is weak, but because it’s powerful, and power without discipline is chaos.

For me, the practical conclusion is simple: if you want AI-assisted coding to work in production, you need a workflow that makes context explicit, validation unavoidable and change reversible. Atomic commits, Git-managed context and TDD 2.0 create exactly that.

The future of software development isn’t autonomous AI. It’s a partnership. And like any good partnership, it works best when everyone knows their role.