Section 01 · Why Patterns Matter
Why choosing the wrong pattern is expensive
A multiagent system built on the wrong pattern does not fail obviously — it ships slowly, runs expensively, and fails intermittently in ways that are hard to reproduce.
Quick answer
The short answer: Multiagent design patterns define how agents communicate, coordinate, and pass state. The pattern you choose determines your system's cost, reliability, and debuggability. Get it wrong and you spend the rest of the project fighting coordination bugs.
In 2026, 57% of enterprises deploy multiagent workflows, but most engineer them ad-hoc — agents calling agents, message queues bolted on, state passed in ways that made sense for the first five calls but break at the sixtieth. The production incidents almost always trace back to a coordination pattern problem, not a model problem.
The good news: there are four patterns that cover the vast majority of production multiagent use cases. Knowing which one fits your problem before you start building saves weeks of architectural rework.
Section 02 · Pattern 1
Sequential: linear pipelines where order matters
The sequential pattern is a chain: Agent A runs, passes its output to Agent B, which passes its output to Agent C. Each step can access the outputs of all previous steps. No parallelism. No routing decisions. The graph is fixed at design time.
Use when
Each step depends on the output of the previous step, the workflow is predictable and does not branch, and simplicity and debuggability outweigh throughput concerns. Document processing pipelines — ingest, extract, classify, summarize, store — are the canonical example.
Avoid when
Steps are independent of each other and could run in parallel. Running independent steps sequentially wastes time and adds latency for no benefit. If steps A, B, and C do not depend on each other, use the orchestrator-worker pattern instead.
Section 03 · Pattern 2
Orchestrator-worker: parallel dispatch for independent subtasks
The orchestrator-worker pattern has a central orchestrator that breaks a goal into independent subtasks, dispatches them to specialized workers in parallel, waits for results, and synthesizes a final output. Wall-clock time is the runtime of the slowest subtask, not the sum of all subtasks.
Use when
The goal can be decomposed into independent subtasks that do not depend on each other's outputs. Research workflows — gather data from 10 sources simultaneously, then synthesize — are the canonical example. The orchestrator dispatches all 10 retrievals in parallel rather than in sequence.
Avoid when
Subtasks are interdependent and must execute in order. The orchestrator-worker pattern adds coordination overhead — the orchestrator must track all worker states, handle partial failures, and manage synthesis. For linear workflows, sequential is simpler and equally fast.
In LangGraph, implement this with the Send API: the orchestrator node fans out to worker nodes by sending them typed messages simultaneously. The graph collects all responses into the shared state before the synthesis node runs. This is now the standard implementation pattern — custom message passing infrastructure is no longer necessary.
Section 04 · Pattern 3
Hierarchical: supervisor agents for complex multi-domain workflows
The hierarchical pattern introduces supervisor agents between the orchestrator and the workers. A top-level orchestrator delegates to domain supervisors. Each supervisor manages the workers in its domain. Results flow back up the hierarchy to the top-level synthesizer.
Use when
The problem spans multiple distinct domains — legal research, financial analysis, and technical evaluation, for example — where each domain has specialized tools and requires domain-specific reasoning. A flat orchestrator-worker structure would need the orchestrator to understand all three domains. A hierarchical structure delegates domain-specific understanding to domain supervisors.
Avoid when
The workflow is single-domain and the added hierarchy creates coordination overhead without adding capability. Hierarchical systems are significantly more complex to debug: a failure in a worker might surface as an ambiguous error at the supervisor level. Introduce hierarchy only when the complexity of the problem justifies it.
Section 05 · Pattern 4
Dynamic handoff: runtime routing based on evolving context
Dynamic handoff allows agents to transfer control to other agents at runtime based on the state of the conversation or task — without a fixed graph. The agent decides at each step who should handle the next step based on the current context.
Use when
The workflow cannot be fully defined at design time because the next step depends on intermediate results. Customer service agents that route to specialized agents based on issue type and severity are the canonical example. The routing logic lives in the model's reasoning, not the graph structure.
Avoid when
The routing logic can be expressed as a fixed graph. Dynamic routing is powerful but creates unpredictability: it is harder to test exhaustively, harder to audit, and harder to debug when the model routes unexpectedly. Prefer fixed patterns when possible and use dynamic handoff only when flexibility is essential.
Section 06 · Implementation
LangGraph: the production default for all four patterns
LangGraph implements all four patterns natively. Sequential chains map directly to linear graph edges. Orchestrator-worker uses the Send API for parallel dispatch. Hierarchical architectures use subgraphs with typed state boundaries between levels. Dynamic handoff uses conditional edges that route based on state values at runtime.
The key properties that make LangGraph the production default: typed state (you cannot accidentally pass malformed data between agents), built-in persistence (agent state survives process restarts), and the checkpoint system (you can inspect, debug, and replay any step of any run). These are the features that matter when something goes wrong at 3am in production.
For how patterns fit into a complete production agentic AI architecture, see my agentic AI consulting service which covers orchestration design as part of full system delivery.
FAQ
Frequently asked questions
What are the main multi-agent design patterns in 2026?
The four canonical patterns are sequential (ordered pipeline), orchestrator-worker (parallel dispatch), hierarchical (supervisor agents managing worker agents), and dynamic handoff (runtime routing based on context). LangGraph supports all four natively and is the production default framework in 2026.
When should I use the orchestrator-worker pattern?
Use orchestrator-worker when your goal can be decomposed into independent subtasks that do not depend on each other's outputs. The pattern runs subtasks in parallel, reducing total wall-clock time to the runtime of the slowest subtask rather than the sum of all subtasks. Research workflows, content generation pipelines, and parallel data enrichment are common use cases.
What is the difference between hierarchical and orchestrator-worker multi-agent systems?
Orchestrator-worker has two levels: an orchestrator and workers. Hierarchical has three or more levels: a top-level orchestrator, domain-level supervisors, and workers. Use hierarchical when the problem spans multiple distinct domains that each require specialized reasoning and tooling. Use orchestrator-worker for simpler parallel dispatch.
How do I implement multi-agent patterns in LangGraph?
LangGraph implements sequential patterns as linear graph edges, orchestrator-worker using the Send API for parallel fan-out, hierarchical patterns using subgraphs with typed state boundaries, and dynamic handoff using conditional edges that evaluate state at runtime. The shared typed state object is the key: all agents read from and write to the same typed structure.