sparc-methodology

SPARC methodology coordinates multiple AI agents through distinct phases—Specification, Architecture, Refinement, Review, and Completion—using an orchestrator agent to manage hierarchical, parallel, or adaptive workflows. Each phase assigns specialized agents (researcher, architect, coder, tdd, reviewer) to ensure systematic progression from requirements through testing to deployment.

SPARC's TDD mode enforces test-first development by having dedicated agents write tests before code, ensuring coverage and correctness from the start. This differs from sequential code generation by embedding quality checks throughout the development cycle rather than reviewing afterward, reducing rework and architectural drift.

Yes. SPARC is explicitly designed for AI development workflows, including multi-agent orchestration and system coordination. It provides 17 specialized modes covering research, architecture, coding, testing, optimization, and debugging—all tailored to handle the complexity of AI systems from initial design through production deployment.

SPARC enables concurrent execution of independent tasks by distributing work across multiple agents simultaneously—researcher and architect can work in parallel, while coders and testers operate on separate modules. This reduces sequential bottlenecks and accelerates delivery compared to single-agent or strictly sequential workflows.

SPARC requires clear project specifications, defined architectural patterns, and designated agent roles within your workflow. Begin by running the orchestrator mode with your task description; the orchestrator automatically assigns specialist agents and manages coordination, so no prior multi-agent setup is necessary.

Yes. SPARC includes a dedicated reviewer agent that conducts systematic code reviews, architectural assessments, and test validation during the Review phase. This enforces consistency, catches regressions, and ensures compliance with defined standards before completion.