From Single Agent to Orchestration: When Multi-Agent Systems Pay Off
When to move from a single LLM agent to multi-agent orchestration, the production patterns that matter, and a Rust coordinator/worker example.
A single LLM agent with a system prompt, a few tools, and a loop works well for demos but breaks down once a task requires genuinely different kinds of reasoning at once. Three failure modes drive the shift to orchestration: context pollution between unrelated sub-tasks, tool overload that hurts selection accuracy, and the inability of one agent to hold conflicting behavioral modes simultaneously.
Production systems mostly rely on four concrete patterns: sequential pipelines, concurrent fan-out/fan-in, handoff between agents, and hierarchical coordinator/worker setups — the last being the most common. A working Rust example shows a coordinator decomposing a request into sub-tasks, dispatching them to worker agents concurrently via join_all, and merging their outputs through a dedicated synthesis LLM call rather than simple concatenation. Giving each worker its own narrow prompt is the actual mechanism that prevents context pollution.
Orchestration isn't free: planning, dispatch, and merge steps add token cost, debugging becomes harder since errors can originate anywhere in the pipeline, and coordination overhead can outweigh the benefit for simple linear tasks. A Gartner-cited pattern notes that many agentic projects get cancelled not because agents fail, but because teams adopted orchestration before the task warranted it. The practical rule: default to a single agent, and only move to orchestration when sub-tasks are genuinely independent, require different tool sets or instructions, or need real parallelism for latency — otherwise a well-scoped single agent wins on cost and reliability.