Multi-agent orchestration consists of coordinating several AI agents, each specialized in a specific task or capability, so that they collaborate in solving a problem that would be difficult to address with a single agent. A central component, often called an orchestrator or coordinator, distributes tasks, manages the flow of information between agents, and consolidates partial results into a final response.
This approach matters because it allows for breaking down complex problems into manageable subtasks and leveraging specialization: one agent can search for information, another can analyze data, and another can draft the result. The most common coordination strategies include:
- Sequential: each agent acts when the previous one finishes.
- Hierarchical: a supervisor agent delegates to sub-agents.
- Parallel: several agents work simultaneously on different parts.
A practical example is a customer service system where one agent classifies the query, another consults the knowledge base, and a third generates the response. The key nuance lies in the communication design: poor coordination management can multiply errors, costs, and latency.