Single-agent products are hitting a complexity ceiling. 2026 is the year multi-agent becomes default — and with it, the need to pick an orchestration pattern. Four patterns dominate the field, with real trade-offs. Here is when to use which.
Why single-agent hits a ceiling
A single agent with 50 tools loaded suffers three failure modes: prompt-window pressure from tool schemas, selection accuracy drops as the tool menu grows, and one bad tool choice derails the whole loop. Splitting the workload across specialised agents addresses all three — at the cost of coordination complexity.
Pattern 1: Supervisor
One orchestrator agent owns the plan. It delegates sub-tasks to specialised workers, collects results, and decides the next step.
user → supervisor → worker_a → supervisor → worker_b → supervisor → answer
- Strengths: easy to reason about, auditability, one place for policy.
- Weaknesses: supervisor becomes a bottleneck; its context grows with every hop.
- Pick when: tasks decompose cleanly, you need predictable behaviour.
Pattern 2: Pipeline
Fixed sequence of stages, each a specialised agent. Classic flow: research → outline → draft → edit. No branching, no backtracking.
- Strengths: deterministic, testable, cheap — each stage uses the cheapest model that handles it.
- Weaknesses: brittle when the task does not fit the stages; no adaptation.
- Pick when: the workflow is well understood (data extraction, content generation).
Pattern 3: Mesh
Peer agents communicate directly via a shared blackboard or message bus. No central supervisor; coordination emerges from posted messages and claims.
- Strengths: scales horizontally, resilient to any single agent dying.
- Weaknesses: debugging is hard, non-termination risks, cost unpredictability.
- Pick when: tasks are parallelisable and heterogeneous (monitoring fleets, distributed research).
Pattern 4: Swarm
Many instances of the same agent work on variants of the task, then a consensus step picks the best result. Think best-of-N sampling at the agent level.
- Strengths: quality on open-ended tasks, trivial parallelism.
- Weaknesses: N-times cost, diversity collapse if agents start identically.
- Pick when: one right answer is worth paying N times.
Comparison table
| Pattern | Coordination cost | Debuggability | Cost | Adaptability |
|---|---|---|---|---|
| Supervisor | High at supervisor | Easy | Medium | High |
| Pipeline | None | Trivial | Low | None |
| Mesh | Distributed | Hard | Variable | High |
| Swarm | Final aggregation | Medium | N x base | Medium |
Failure modes per pattern
- Supervisor: context explosion as the plan grows. Mitigate with sub-agent summarisation.
- Pipeline: edge inputs produce garbage at stage 1 that propagates unstopped. Mitigate with validators between stages.
- Mesh: non-termination loops where agents keep delegating to each other. Mitigate with a step budget per task.
- Swarm: diversity collapse — all instances land on the same wrong answer. Mitigate by varying the prompt or temperature per worker.
Picking by use case
| Use case | Pattern |
|---|---|
| Customer support triage | Supervisor |
| Document generation | Pipeline |
| Research + due diligence | Supervisor + swarm at leaves |
| Monitoring a fleet | Mesh |
| Code generation with verification | Swarm |
What the Claude Agent SDK gives you out of the box
The Claude Agent SDK supports supervisor and pipeline patterns directly; mesh and swarm need some assembly but the primitives are there. See the builder guide for the minimal wiring.