orchestrator

Multi-agent coordination for complex codebase changes is handled through memory-informed planning, routing, and quality gates. The orchestrator delegates specialized tasks to different agents, executes them in parallel, and applies validation to ensure robust delivery across multiple modules.

Memory-informed planning uses stored context to guide task routing and delegation across multiple agents. By searching memory before execution, the system classifies requirements, builds an accurate plan, and applies retrospective learning after completion to improve future sessions.

Automated task delegation and parallel execution are supported for large codebase changes. The orchestration workflow classifies the refactor requirements, delegates specialized subtasks to different agents simultaneously, and validates the aggregated output against quality gates.

Multi-agent orchestration is designed specifically for cross-file tasks requiring collaboration across agents. It manages the entire workflow from memory search and planning through delegation, execution, and validation to coordinate complex software changes across multiple modules.

Automated multi-agent execution is validated through integrated quality gates applied after the delegation and execution phases. These gates inspect the aggregated results of the coordinated agents to ensure the complex software changes meet requirements before retrospective learning updates memory.

Multi-agent orchestration should be used for complex software changes, feature refactors, and cross-file tasks requiring collaboration across agents. Single-agent automation lacks the memory-informed routing, parallel delegation, and quality gates needed to coordinate large codebase changes.