What is multi-agent orchestration?
Multi-agent orchestration is coordinating several specialized AI agents to complete one goal together. A lead 'orchestrator' agent breaks the goal into tasks and delegates each to a 'subagent' with the right role and tools, then combines their results, much like a manager coordinating a team.
Multi-agent orchestration applies an old idea to AI: divide and conquer. One agent trying to do everything gets slow, expensive, and error-prone. A team of focused agents, coordinated well, is faster and more reliable.
Orchestrator and subagents
- Orchestrator, the lead agent. Plans the work, delegates tasks, and combines results.
- Subagents, specialists it delegates to, each with a narrow role and the tools for it.
Independent tasks can run in parallel, and each agent keeps a small, relevant context instead of one bloated conversation, which improves both speed and accuracy.
Orchestration in Oido
Oido supports orchestrator agents with subagents, so an AI employee can delegate specialized work to others on the team. Each agent can run on its own model provider and use its own tools, matched to the job.
Frequently asked
Specialization and reliability. A focused subagent with a narrow role and the right tools outperforms one agent trying to do everything. Splitting work also lets independent tasks run in parallel and keeps each agent's context small and relevant.
The orchestrator is the lead agent. It does not do the detailed work itself, it plans, delegates each task to the right subagent, and synthesises the results into a final answer or action.
A subagent is a specialized agent the orchestrator delegates to, for example a research subagent or a data-entry subagent, each with its own role, tools, and often its own model.