pcc

Multi-file software development tasks are automated by coordinating parallel AI agent swarms that explore the codebase, synthesize findings, and implement code dynamically. This ensures efficient task understanding and parallel execution for complex features.

Parallel agent coordination is a process where multiple AI agents are spawned simultaneously to scout codebase architecture and implement code. It uses Sonnet agents for exploration and Opus agents for implementation to maximize parallel processing efficiency.

Implementation planning for multi-module features involves synthesizing parallel exploration findings, identifying ambiguities, and asking user clarification questions. This generates a detailed plan that delegates coding tasks to multiple implementation agents.

Parallel processing supports automated verification and code simplification by running multiple agents simultaneously after implementation. This orchestrates testing, code review, and structural refinement to ensure code quality and maintainability.

Parallel agent coordination works for exploring dependencies and configurations by spawning multiple exploration agents to scout the codebase. These agents dynamically identify relevant architecture, features, tests, and configuration files in parallel.

Orchestrating software development with parallel AI agents requires clear user clarification for identified ambiguities before planning. Complex tasks lacking defined architecture or dependencies may reduce the efficiency of dynamic agent spawning and parallel implementation.