sparc-methodology

SPARC methodology orchestrates AI agents through five structured phases—specification, architecture, refinement, review, and completion—ensuring systematic coordination and quality at each stage. It uses 17 specialized modes and TDD workflows to automate the full development lifecycle from concept to deployment.

Test-driven development (TDD) follows the Red-Green-Refactor cycle: agents write failing tests first, implement minimal code to pass them, then refactor for quality. SPARC automates this cycle within parallel multi-agent orchestration to ensure robust, quality-first code throughout development.

Yes. SPARC uses claude-flow to coordinate specialized agents across all phases, including specification writing, architecture design, test-driven refinement, code review, and deployment. Parallel execution and persistent knowledge sharing reduce time while maintaining consistency and quality.

SPARC implements memory-based coordination and modular API contracts across 17 specialized modes, allowing agents to share context and enforce consistent interfaces. This parallel execution model prevents duplicated effort and ensures architecture decisions flow through refinement and review phases.

SPARC enforces systematic TDD, parallel agent execution, and persistent knowledge sharing across all phases, reducing errors and development time. Traditional approaches often silo phases; SPARC integrates specification, architecture, refinement, review, and deployment into one coordinated workflow.

SPARC abstracts multi-agent complexity through claude-flow and predefined modes for common tasks like coding, architecture, and testing. The methodology handles agent coordination; you define the feature or problem, and SPARC routes it through the appropriate development phases and specialized agents.