"AI code generation tools promise faster development, but often create quality issues, integration problems, and delivery delays. In this article, I describe a structured Plan-Do-Check-Act (PDCA) framework for human-AI collaboration that I've been refining over the last six months after working with agents in an unstructured process for over a year before that. Using this PDCA cycle, I believe I can better maintain code quality while leveraging AI capabilities."
"Apply structured goal-setting cycles to AI coding sessions: Set clear, observable success criteria for each session using plan-do-check-act principles and adjust course based on results. Use structured task-level planning with AI: Have the agent analyze the codebase and break large features into small, testable chunks that can be completed in short iterations to prevent scope creep. Apply a red-green unit test cycle to AI code generation: Have the agent write failing tests first, then production code to make them pass,"
Apply a Plan-Do-Check-Act (PDCA) cycle to AI-assisted coding sessions by defining clear, observable success criteria and adjusting course based on results. Require agents to analyze the codebase and break large features into small, testable chunks that fit short iterations to prevent scope creep. Use a red-green unit-test cycle where the agent writes failing tests first, then production code to make them pass, creating a feedback loop that reduces regressions. Insert validation checkpoints for completion analysis before moving to the next iteration. Hold daily five-to-ten-minute micro-retrospectives with the agent to refine prompts, interactions, and accountability.
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