Multi-Agent Orchestration

Multi-agent orchestration is the practice of decomposing software development work across multiple specialized AI agents that run in parallel, each handling a distinct task such as coding, testing, reviewing, or documentation within a shared codebase. Rather than a single agent doing everything sequentially, an orchestrator coordinates the group to maximize throughput and maintain consistency.

Key characteristics of multi-agent orchestration include:

1. Role Specialization: Each agent is assigned a focused responsibility. One agent writes feature code, another generates tests, a third handles documentation, and a fourth performs code review. Specialization reduces context-switching and improves output quality for each task.

2. Merge Coordination: The central challenge is preventing conflicts when multiple agents modify the same codebase simultaneously. Orchestrators must manage branching strategies, resolve merge conflicts, and ensure that parallel changes remain compatible.

3. Real-World Implementations: Steve Yegge's Gas Town manages 20-30 parallel Claude Code agents with persistent identity (Beads), git-backed state, and role-based workers including a Mayor (orchestrator), Polecats (scouts), and Crew (implementers). StrongDM's Factory runs autonomous agents with scenario-based validation against simulated environments.

4. Orchestrator Patterns: Anthropic's research identifies the trend of organizations moving from single agents to specialized groups under an orchestrator agent that plans, delegates, and synthesizes results from subordinate agents.

5. Monitoring and Coordination: A Flow Manager coordinates task assignment and sequencing across agents, while an Agentops Dashboard provides real-time visibility into each agent's status, cost, and output quality.

6. Current State: Multi-agent orchestration is emerging but growing fast. It requires advanced developer skills to configure, monitor, and debug because failures in one agent can cascade across the entire group.