What is multi-agent orchestration? (And when one agent is enough)

How the pattern works

Multi-agent orchestration is the pattern where one lead agent holds the goal, splits it into sub-tasks, and dispatches them to narrower agents that run at the same time. Anthropic calls the shape orchestrator-workers: a lead agent plans, spawns subagents, and merges what comes back. The operating benefit is concurrency. Four agents checking four suppliers in parallel finish in roughly the time one agent needs for a single supplier.

What the fan-out costs

The bill scales with the number of agents. Anthropic measured multi-agent systems using about 15 times the tokens of a chat conversation, and agents generally using about 4 times chat. The same team reported a lead agent with Sonnet subagents beating a single agent by 90.2% on an internal research eval. The spend can pay for itself, but only on work valuable enough to justify it.

Where it breaks

Two failure modes matter at budget time. Agents converge instead of diversifying: in one Anthropic study, 18 of 30 agents independently created a git branch with the same name, so an isolated mistake becomes a systemic one rather than getting caught by a second opinion. And every handoff is lossy, because what a sub-agent passes back is a summary, not the full working context it had. Gartner expects over 40% of agentic AI projects to be cancelled by the end of 2027 on cost and unclear value, and unnecessary fan-out is a fast route there.

If the work is one sequence of steps against a single source of truth, keep one agent and put the budget into tools and approval gates. Add agents when the task genuinely splits into lanes that do not need each other's context, such as parallel research, review, or per-account outreach.