I have built agent systems with LangGraph, AutoGen, and CrewAI, and I have built features where the right answer was a single well-engineered prompt. The most useful thing I can tell a PM about multi-agent orchestration is this: most products that reach for it do not need it. Here is how to tell when you actually do.
What "multi-agent" really means
An agent is an LLM given a goal, a set of tools, and the autonomy to decide which tool to call and when. Multi-agent means several of these, each owning a slice of the problem, passing work between them. A research agent gathers, a reasoning agent analyzes, a writer agent produces output.
The appeal is obvious: decompose a hard task into specialists. The hidden cost is also real: every agent boundary is a place where context gets lost, latency stacks, and errors compound.
The decision rule
Use a single prompt or a simple chain when the task is one coherent reasoning step, even a complex one. Reach for multiple agents only when the task has genuinely distinct phases that need different tools or different reasoning, and the phases are hard to express as one prompt.
A document-analysis pipeline where each stage needs a different tool — retrieval, then computation, then synthesis — is a real multi-agent case. "Summarize this and make it friendlier" is not; that is one prompt.
Scope it like a team, not a magic box
The mental model that works: treat the agent system like a small team you are managing. Each agent gets a clear job description (its prompt and tools), a clear hand-off (what it passes forward), and a clear definition of done. The failure modes are management failures — an agent with a vague job, a hand-off that drops context, no one accountable for the final quality.
On my own builds, the router pattern earned its keep: one agent whose only job is to decide which downstream path a request takes — compute, retrieve, or answer directly. That single decision, made explicit and testable, prevented the most common failure, which is the system confidently using the wrong tool.
What the PM owns
Three things:
- The decomposition. Where are the seams between agents? Draw them wrong and you get lost context and stacked latency. This is a product decision, not an engineering one.
- The fallback. What happens when an agent fails or loops? Every agent needs a timeout, a retry budget, and a graceful degradation path. Users should never see a stuck system.
- The evaluation. A multi-agent system needs evals at the seams, not just end-to-end. When the final output is wrong, you need to know which agent dropped the ball.
The honest summary
Multi-agent orchestration is a powerful tool and an over-applied one. The frameworks — LangGraph, AutoGen, CrewAI — are good. They will also happily let you build a five-agent system for a problem one prompt could solve, at five times the latency and cost. The PM's job is to keep the system as simple as the problem allows, and no simpler.