Agents
Agent orchestration
Coordinating multiple AI agents, tools, and steps into one workflow — via planners, routers, and frameworks like LangGraph — to solve complex tasks reliably.
Agent orchestration is the coordination of multiple AI agents, tools, and steps into a coherent workflow so that a task is solved more reliably than a single model could manage alone. An orchestrator decides which step runs next, routes data between components, and synthesizes their results.
In practice there are two broad approaches. A workflow connects models and tools through predefined code paths — patterns such as prompt chaining, routing (classifying an input and then directing it to a specialized task), and parallelization. By contrast, in agentic AI the model dynamically directs its own process; the orchestrator-workers pattern uses a central LLM to break down a task, delegate it to worker agents, and combine their output. Tools are frequently invoked through protocols like MCP.
Through 2025–2026, orchestration became central to production systems, with frameworks such as LangGraph, AutoGen, and CrewAI modeling it as a state graph. Open challenges include reliability over long execution paths, cascading errors, and coordination overhead.