A multi-agent system does not need an LLM manager to choose every handoff when its workflow is already knowable. Represent the work as a graph: nodes perform agent, tool, or code steps; edges define the next step; and shared state carries inputs and results. Use a supervisor when the next task really does depend on open-ended judgment—not as a mandatory layer.
What graph-based orchestration means
A graph makes workflow control part of the application instead of delegating every transition to a manager agent. Each node does a unit of work, which can be an agent, a tool call, or deterministic code. Edges connect those units and specify what happens next. State is the structured record passed through the graph, holding the original request and the intermediate results later steps need.
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LangChain’s multi-agent overview describes agents as independent actors that may have their own prompts, models, tools, or code; in this framing, agents are nodes, connections are edges, and graph state is how they communicate. The framework is one example of this architecture, not the only way to build a graph-based workflow. LangChain’s multi-agent overview
When a graph can replace manager decisions
If the process has known stages and decision rules, encode those rules as transitions. A fixed edge handles an inevitable next step; a conditional edge chooses a path based on state or a rule’s output. A loop can send work back for review or repair, provided it has a clear exit condition and a limit. Parallel branches can handle independent subtasks, after which the graph joins their outputs for synthesis.
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For example, a research workflow might extract facts, validate them, draft an answer, and run a review. If validation fails, a conditional edge can route the result to a repair step; if it passes, execution proceeds to drafting. Independent checks can run in parallel, with their findings added to state before review. The application—not a general-purpose manager call—owns these known transitions.
LangChain’s current workflow documentation describes sequential steps, conditional branches, loops, and parallel execution, and presents custom workflows as a way to combine deterministic logic with agent behavior. Its workflows-and-agents guide also describes workers writing results to shared graph state. Custom workflow documentation and Workflows and agents guide
Choose the pattern that matches the work
| Pattern | How control flows | Good fit | Trade-off |
|---|---|---|---|
| Explicit graph with conditional routing | The application selects the next node from state or a rule’s output. | A known process with branches, validation gates, or bounded loops. | Developers must model transitions and state deliberately. |
| Parallel worker graph | Independent worker nodes run subtasks and contribute outputs to shared state. | Work that can be split into independent pieces and combined later. | Results still need coordination and synthesis; parallelism is not useful when tasks depend on one another. |
| Supervisor | A manager agent selects or routes work to individual agents. | Open-ended delegation where the next specialist or task depends on the request or an intermediate result. | Adds a central routing decision and its associated model call and failure mode; the size of any cost or latency impact must be measured for the workload. |
| Hierarchical graph | A graph or team is nested as a node in a larger graph. | A system that needs composition or layers of responsibility. | More structure can make implementation and debugging more complex. |
These are design choices, not a universal ranking. A supervisor is a documented pattern: LangChain’s January 2024 overview describes it as responsible for routing to individual agents and also describes hierarchical teams built from graph-based agents. Its pattern vocabulary remains useful, while implementation details should be checked against current documentation. LangChain’s January 23, 2024 overview
How to design a graph workflow
- Start with the smallest useful process. Write down the request’s path from input to final output, including the decisions that can change that path.
- Define the state. Include durable inputs and the intermediate values later nodes require—for example, the request, extracted facts, assignments, worker results, and final output.
- Turn operations into nodes. Separate agent steps from deterministic code and tool calls where that makes responsibilities or failure handling clearer.
- Choose edges deliberately. Use fixed edges for inevitable transitions and conditional edges for explicit decisions. Add parallel branches only when the subtasks can proceed independently.
- Bound review loops. Specify what counts as completion, what happens when a check fails, and how many repair attempts are allowed so a workflow cannot loop indefinitely.
- Assign state ownership and join results. Decide which node writes each value, how parallel outputs are collected, and what the synthesis step receives.
When a supervisor is still the right choice
Use a manager agent when the system cannot know the next task in advance and must interpret context to choose a specialist or break down the problem. That is different from asking a manager to select among transitions the application already knows. If a workflow is stable and auditable, application-level conditions can own routing. A hybrid is also possible: let a graph govern the known process while a supervisor or specialist agent handles the sections requiring judgment.
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A graph makes paths visible and configurable; it does not guarantee better answers, correct routing, fewer failures, or lower cost. “Scale” needs a specific measure: throughput, concurrent work, end-to-end latency, model or infrastructure cost, failure recovery, or maintainability. Parallel branches may reduce elapsed time when tasks are independent, but dependencies, model and tool latency, scheduling, and result aggregation all affect the outcome. The cited architecture guidance does not establish a general benchmark showing that graphs outperform supervisors at scale.
Evaluate the workflow against the actual constraint. Track where time and failures occur, inspect whether transitions and state updates match expectations, and test representative inputs, including branch and recovery cases. LangGraph’s reference describes it as a low-level framework for long-running, stateful agents and recommends it for advanced needs involving deterministic and agentic workflows, customization, and controlled latency; that is vendor guidance, not a performance guarantee. It distinguishes LangGraph from higher-level prebuilt agent architectures. LangGraph reference
For tracing and evaluation, LangSmith is an optional example: the LangGraph reference identifies it as a LangChain developer platform for testing and monitoring LLM applications. Choose monitoring that lets your team examine the transitions, state, and outcomes relevant to its own workflow.
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