## Why a single agent is not enough
A single agent that has to plan, retrieve, reason, call tools and write the final answer becomes a bottleneck very quickly. Prompts grow huge, context windows blow up, and the model loses focus halfway through a task. In 2026, the pattern that scales is multi-agent: small agents with sharply defined responsibilities collaborating through a clear protocol.
The trick is not adding more agents. It is choosing the right orchestration pattern for the problem.
## The supervisor pattern
In the supervisor pattern, one agent acts as the manager and delegates to specialist worker agents. The supervisor receives the user request, picks the right specialist (research, code, finance, support), passes the task with a clean brief and validates the result.
This pattern is excellent for support-style workflows where intent classification matters. The supervisor stays small and fast, while the specialists can be heavier and more domain specific. The risk is that the supervisor becomes the new bottleneck. Keep it focused on routing and validation, not on doing the actual work.
## The swarm pattern
In a swarm, agents collaborate as peers. Each one can hand off the conversation to another agent that is better suited to the next step. Think of a triage workflow where a billing agent might hand off to a refunds agent, which might hand off to a fraud-check agent.
Swarms feel natural for conversational interfaces because the user does not see the handoff. The challenge is preventing infinite loops. Always include a maximum hop count and a clear escalation path back to a human or a default agent.
## The graph pattern
For complex pipelines with branching logic, model the workflow as a graph of nodes. Each node is an agent or a deterministic step. Edges define transitions, often based on the output of the previous node.
This pattern shines for processes that mix LLM steps with rule-based steps, such as document processing, claim handling or onboarding. It is also the easiest to test, because each node can be evaluated in isolation. Modern frameworks such as LangGraph and OpenAI\u2019s agent SDKs make this style very ergonomic.
## Choosing between them
A simple rule of thumb:
- If the work is **mostly classification then specialist execution**, use a supervisor.
- If the work is **a conversation that flows between domains**, use a swarm.
- If the work is **a pipeline with conditional steps**, use a graph.
Most real systems combine all three. A graph at the top with a supervisor inside one node and a swarm inside another is perfectly normal.
## Communication and state
Agents need a shared substrate. Pick one of two approaches:
- **Shared state object** that every agent can read and write. Easier to debug, but requires discipline to avoid spaghetti.
- **Message passing** with explicit envelopes. Cleaner contracts, but more boilerplate.
Whichever you choose, log every message and every state mutation. When an agent run goes wrong, you will spend most of your time reading those logs.
## Cost and latency
Multi-agent systems multiply token usage. Three agents in series easily means three times the cost. Combat this with smaller models for routing and validation, caching of intermediate results and parallel execution wherever the dependency graph allows.
## Production checklist
Before you ship a multi-agent system, make sure you have:
- A maximum step budget per request.
- A timeout per agent.
- A fallback path if an agent errors.
- Tracing that shows the full call graph, not just the final answer.
- An evaluation suite that exercises the orchestration, not just the individual agents.
Multi-agent design is one of the highest leverage skills in enterprise AI right now. Get the orchestration right and the rest of the system gets dramatically simpler.