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Multi-agent orchestration parses a plan into tasks and dependencies, then dispatches a swarm of specialized AI workers to implement the work in parallel. It coordinates phase-gated execution with quality checks to ensure safe, auditable collaboration.

Managing task dependencies in a multi-agent workflow involves parsing a high-level plan into discrete tasks, assigning roles, and executing work in waves. The system tracks per-task ownership and ensures dependencies resolve before advancing phases.

Git worktree mode is supported for isolated parallel task execution across AI agents. It enforces branch safety and serializes git operations through commit and merge brokers, preventing conflicts when multiple agents modify code simultaneously.

To enforce quality gates before merging multi-agent code changes, the system applies ward checks and optional critiques during phase-gated execution. It compacts results and finalizes changes only after all defined quality checks pass.

A pre-defined plan file is required to start a multi-agent software delivery session. The orchestration engine parses this plan into actionable tasks, dependencies, and role assignments to summon the correct swarm of workers.

Use a multi-agent swarm approach instead of a single AI agent when a feature is too large for one agent to complete alone. It prevents bottlenecks and miscoordination by distributing planning, execution, and quality assurance across specialized workers.