When multiple AI agents work on the same codebase, they often overwrite each other's changes. Instructing agents to create a new git branch (a "work tree") for each feature enables parallel development without conflicts, mimicking a human engineering team's workflow.
Instead of manually reviewing every line of AI-generated code, mandate that the agent provides proof of its work. This can be before-and-after screenshots, screen recordings of the feature in action, or performance metrics. This simplifies review, especially for non-technical managers.
Integrate a third-party code review agent (e.g., Greptile) to score the AI's work. The workflow instructs the agent to automatically re-enter the "build" phase and address feedback until it achieves a perfect score. This creates a self-correcting system requiring minimal human intervention.
The viral concept of a "software factory" is not a product you buy, but a model-agnostic workflow built on skills and domain knowledge defined in simple markdown files. This demystifies the term, showing anyone can create their own by structuring their AI agent's development process.
AI models often produce functional but sloppy code. To combat this, embed a specific software architecture, like a "service layer architecture," into the agent's instructions. This forces the AI to write clean, organized, and human-readable code crucial for long-term maintenance and collaboration.
The most effective way to manage AI software development is to mirror a physical assembly line. "Isolate" is the custom order station. "Build" is the assembly line. "Prove" is quality control testing. "Ship" is the final packaging and delivery. This analogy provides a robust mental model for managing complex AI workflows.