Don't Trust an AI Plan Until It Closes the Loop
AI-generated plans that look complete can silently reintroduce regressions when they ignore integration constraints and unverified assumptions. Skipping the system-level check costs teams broken flows and newly re-fixed old bugs.
AI can generate a structured solution with code in seconds, but a coherent answer is not the same as a verified one. In a recent planning session, a stalemate emerged: solving one problem created another, and several AI-proposed directions failed to close the loop in the existing system, even bringing back fixed bugs. One side leaned on the AI output; the other refused to proceed without a written plan that defines boundaries, dependencies, and verification steps.
A building-block metaphor drives the point: each block may be sound, but without proper interlocking the stack collapses under change. A local change that makes sense on its own can break another workflow or reintroduce old issues. AI can propose and assist, but the context, constraints, and final judgment about whether the plan holds in the actual environment have to come from the people who own the system.
The proposed workflow is to write and review the full plan first, then let AI generate code, then review the result against both new and old problems. That includes checking memory leaks, out-of-memory conditions, and multi-threading races when the change touches affected code. Writing the plan forces a deeper understanding of the system, and AI acts as an ability amplifier rather than a replacement for that understanding.
The building-block analogy generalizes beyond AI: correctness of individual components doesn't guarantee correctness of the assembly, which keeps system-level regression checks necessary even as AI speeds up local code generation.
When one teammate treats AI as an authority, teams need an explicit checkpoint that moves from 'what AI said' to 'does it hold here' to avoid skipping the hard judgment.
The proposed division of labor makes AI a force multiplier for engineers who already know the system, not a substitute for that knowledge; the human supplies context and the final verification.