LLM Agent Orchestration

LLM agent orchestration studies how to organize language models, tools, and specialized agents into systems that can solve tasks requiring multiple steps. Common approaches include sequential and hierarchical pipelines, graph-based workflows, parallel delegation, adaptive routing, and plan–execute–reflect loops, with memory and verification used to manage state and catch errors. Work is evaluated on tasks such as coding, web research, tool use, and domain workflows, using measures that include success, cost, latency, and reliability. A central open tension is that added coordination can improve difficult tasks but also increase cost and failure opportunities, and its benefits depend on the model, task structure, and quality of execution controls.