Have you ever worked on a project where one person handled research, another prepared the content, and someone else checked the final work? Dividing responsibilities makes it easier to manage different parts of the same project.<\/p>\n\n\n\n
AI agents can work in a similar way. One agent can collect information, another can write a report, and a third can check the facts. But someone needs to decide who does what, when to pass the work forward, and how to bring everything together. That’s where AI agent orchestration comes in.<\/p>\n\n\n\n
Instead of making one AI agent handle every task, orchestration allows multiple specialized agents to work together toward a common goal.<\/p>\n\n\n\n
This approach is gaining attention. According to Databricks’ 2026 State of AI Agents report<\/a>, usage of multi-agent workflows on its platform grew by 327% between June and October 2025.<\/p>\n\n\n\n So, how do these agents coordinate their work, and what makes one orchestration pattern different from another? Let’s understand how AI agent orchestration works, which frameworks developers use, and how beginners can build their own multi-agent workflows.<\/p>\n\n\n\n