Assigning a single AI model to handle all stages of a coding pipeline—planning, writing, reviewing, and testing—causes the same blind spots to follow the code from the first line to the last commit, as the reviewer inherits the coder's defects. To address this, the article recommends assigning a different model to each pipeline stage. The method involves picking a model strong at reasoning for read-only planning, then handing the plan to a fast, cheap, code-tuned model for coding. The review should be routed to a model that did not write the code, and a cheap model can review once clear rules are in place. The reviewing model should extend the test suite with edge cases the author missed. Record which model handled each stage in an Architecture Decision Record (ADR) to spot repeating blind spots, and rotate pairings every few months. Apply TDD adversary rules: one model writes the test (Red), another writes the simplest implementation (Green), and a third decides if refactoring is worth it (Refactor). Benefits include catching more defects, avoiding vendor lock, matching cost to complexity, widening test coverage, keeping personas honest, and preventing style lock-in.