boris-tane-workflow

Structured AI-assisted coding prevents scope creep by enforcing phase separation across research, planning, and implementation. You use an annotation cycle to refine plans and persistent artifacts to control execution before touching multiple files or systems.

The research-plan-implement workflow is a methodology that structures AI coding into distinct phases, requiring deep research and persistent planning artifacts before guarded execution. It minimizes misaligned implementations by keeping human judgment in planning and review.

Planning multi-file features with AI safely requires explicit guardrails and feedback loops that separate research, planning, and implementation. Persistent planning artifacts and an iterative annotation cycle refine the plan before any controlled execution begins.

Use a structured AI coding workflow for features touching multiple files or systems in mature codebases, where uncontrolled generation risks costly mistakes. It enforces phase separation and human review to prevent misaligned implementations and scope creep.

Persistent planning artifacts for AI coding are created during the research and planning phases using an annotation cycle. These documents record research, plans, and showboat outputs to prove the workflow works, enable auditing, and guide guarded execution.

A phase-driven AI coding workflow supports iterative development through an explicit annotation cycle that refines plans before implementation. Phase separation controls execution while feedback loops allow plan adjustments without uncontrolled code generation.