When and How to Move from Single AI Agents to Multi-Agent Orchestration

Single AI agents work well for simple tasks but struggle when a job requires multiple types of reasoning simultaneously, leading to issues like context pollution, tool overload, and lack of behavioral separation. Multi-agent orchestration addresses this by splitting complex tasks among a coordinator and specialized worker agents, each with a focused role and limited toolset. Four main orchestration patterns exist in production: sequential pipelines, concurrent fan-out, agent handoffs, and hierarchical coordinator-worker setups, with the last being most common. Frameworks such as LangGraph, CrewAI, and the OpenAI Agents SDK all implement these patterns differently but share the same underlying architecture. A working code example in Rust demonstrates a hierarchical setup where a coordinator decomposes a research request and dispatches sub-tasks to specialized workers concurrently before merging their outputs.