Multi-agent systems: when one agent is not enough.
A second agent adds power and adds cost, latency, and debugging surface. Here is when multi-agent systems earn their keep, the orchestration patterns that work, and the honest case for keeping it to one agent.
A multi-agent system is an architecture where two or more LLM-driven agents, each with its own role, tools, and context, coordinate through an orchestrator or handoffs to complete a task that one agent cannot reliably do alone.
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When one agent is not enough
A single agent struggles when a task spans distinct skills, needs separated context windows, or has steps that should run in parallel. Splitting the work into specialist agents keeps each one focused, but every split adds coordination overhead, so the bar for adding an agent should be real, not architectural fashion.
- The task spans clearly different skills or tools
- One context window cannot hold all the state
- Steps are parallel or need independent retry
Low confidence, policy exception, or protected data.
Orchestration patterns that hold up
Most production multi-agent systems use one of a few shapes. A planner decomposes the goal and delegates to workers. Specialist agents each own a domain and a tool set. Handoffs pass control and context from one agent to the next. The pattern should match the workflow, not the other way around.
- Planner and worker: one decomposes, others execute
- Specialist agents: each owns a domain and its tools
- Handoffs: explicit transfer of control and context
Inputs, systems, owners
Tools, prompts, permissions
Known cases and edge cases
Approvals, traces, rollback
The tradeoffs you are actually buying
Every extra agent multiplies token cost, adds network round-trips and latency, and makes failures harder to trace because a bad output can originate three hops upstream. Multi-agent only wins when the accuracy or parallelism gain outweighs that tax, and you can only know that by measuring both designs.
- Cost: more agents means more tokens per task
- Latency: sequential handoffs add round-trips
- Debuggability: failures are harder to attribute
p95 latency 1.2s
eval pass 12/12
rollback ready
When a single agent is the better choice
If the workflow fits one context window and one tool set, a single agent with good prompts and tools will be cheaper, faster, and far easier to debug. Many teams reach for multi-agent to fix a reliability problem that a better eval suite, clearer tools, or retrieval would solve. Start with one agent and split only when you can name the constraint.
- The task fits one context and tool set
- You cannot yet name the specific limit you hit
- A better eval, prompt, or tool would fix it instead
Customer says the order arrived damaged and asks for a refund.
Source: ZendeskHow Gaper builds multi-agent systems
We start with the simplest design that works and add agents only when a measured limit forces it. Whatever the shape, the system ships with evals on each agent and the orchestrator, guardrails and human approval on risky actions, traces across every handoff, and an owner. You get the code and the runbook, not a black box.
- Simplest design first, split only on a measured limit
- Evals and traces on every agent and handoff
- Guardrails, human approval, and an owner from day one
Concrete places agents earn their keep.
Policy matched. Refund ready for approval.
Planner / worker
An orchestrator breaks a goal into subtasks and dispatches them to worker agents, then assembles the results. Good for variable, multi-step jobs like research or report generation.
Specialist agents
Each agent owns one domain and its tools: a SQL agent, a docs agent, a code agent. A router picks the right specialist per request.
account score
Handoffs
One agent does its part, then transfers control and context to the next, like triage to resolution in support. Control moves; state travels with it.
Parallel fan-out
The orchestrator runs several agents at once on independent subtasks, then merges outputs. Cuts latency when steps do not depend on each other.
Evaluator / critic
A second agent reviews the first agent's output against criteria and sends it back for a fix. Buys accuracy at the cost of extra passes.
Hierarchical teams
A lead agent coordinates sub-orchestrators, each managing its own workers. Reserve this for genuinely large workflows; the coordination cost is real.
Common questions.
What is a multi-agent system?+
When should I use multiple agents instead of one?+
What are the main multi-agent orchestration patterns?+
What are the downsides of multi-agent systems?+
Is a single agent ever better than a multi-agent system?+
How does Gaper decide between one agent and many?+
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