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AI-assisted software development workflows require structured, intent-driven phase management to avoid wasted cycles. Using a cycle-based approach enforces phase discipline and explicit acceptance criteria to keep teams focused and prevent unstructured AI generation.

TDD-centered generation for AI coding sessions is best structured by limiting one task per session. This reduces context switching and improves quality by advancing through focused phases with clear, evidence-based criteria and tests.

To start a new feature cycle using CLI tooling, initialize the project environment with a setup script, then create a new cycle by passing the feature name to a dedicated CLI command. This uses YAML frontmatter and cycle templates to track progress.

No, intent-driven development cycles forbid coding during the Focus, Orchestrate, and Refine phases. These phases enforce problem clarity and task breakdown, while actual code generation is reserved for the Generate phase.

AI software development requires clear problem statements and phase discipline to ensure teams define clear problems and maintain focus. Without explicit acceptance criteria and structured cycles, AI generation lacks direction and produces lower quality output.

Yes, YAML frontmatter and cycle templates are required to initialize, track progress, and surface learnings across projects. They provide the structured metadata and acceptance criteria needed to advance through development phases.