Multi-Agent Orchestration
Overview
Multi-agent orchestration studies how to divide work among AI agents, assign roles, manage communication, and combine results for tasks that are too complex or long-running for one agent. Common approaches include hierarchical planner–worker systems, dependency graphs and parallel execution, adaptive routing among specialized agents, and decentralized coordination through markets or peer networks. Researchers also develop verification, shared memory, recovery, and policy controls, evaluating systems on coding, research, enterprise workflows, scientific computing, and robotics. A central tension is that more coordination can improve coverage and reliability but also adds latency, cost, and new failure or safety risks, so effective systems must adapt their structure to the task and operating constraints.
Latest papers
207 papers30 papers in the last four weeks, up 76% on the four weeks before. 0.2% of all new papers.