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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsDynamic multi-agent workflows coordinate work by deciding which agents should act, what each agent is responsible for, what information it receives, and how its result returns to the rest of the workflow. The key design choice is ownership: a manager can call specialists and remain responsible for the final answer, or it can hand control to a specialist that takes over the next branch. When the work has strict ordering, conditions, or loops, an explicitly defined graph can make those paths easier to control.
Adding agents alone does not make a workflow better. Reliable coordination depends on bounded tasks, explicit result formats, clear rules for dependencies, and enough observability to see what happened and recover when a result is missing or conflicts with another.
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What a multi-agent workflow coordinates
A multi-agent workflow is the orchestration design around a set of agents. It determines which agents run, in what order, how the next task is selected, what context or artifacts move between tasks, and who is accountable for the response delivered to the user. Agents are not automatically sharing one complete, durable memory just because they belong to the same workflow.
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Think of orchestration as the work plan and agents as workers with bounded responsibilities. A coordinating component may ask a specialist to answer a narrow question, pass its result to another agent, or use the result to decide what should happen next. Some choices can be made dynamically at runtime; other workflows encode the path in advance using program logic or a graph. OpenAI’s orchestration guide describes the central distinction as whether specialists take over the conversation or stay behind a manager.
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This matters because task routing and answer ownership are separate questions. A specialist might discover a fact, but the workflow still needs to decide whether the manager, the specialist, or a separate final stage is responsible for resolving disagreements and producing a complete response.
Choose who owns the work after a specialist runs
Start by deciding who should control the next step and who should speak for the workflow. The two common patterns are a manager that calls agents as tools and a handoff that transfers control to another agent. A workflow can combine them, but each branch should have an understandable owner.
| Pattern | Who controls the next step? | Best fit | Main trade-off |
|---|---|---|---|
| Manager with agents as tools | The manager remains in control and receives specialist results. | Several bounded contributions must be checked and combined into one user-facing answer. | One synthesis owner is clear, but the manager must evaluate and integrate the returned work. |
| Handoff | Control moves to the receiving specialist, which owns the next branch or response. | A specialist should take over because the next part of the conversation belongs to its role. | Ownership moves with control; make the handoff conditions and needed context explicit. |
| Explicit graph or state-machine workflow | The encoded workflow determines transitions, conditions, and execution structure. | The task needs a prescribed sequence, fan-out, conditional branches, or cycles. | Paths are more explicit, but the workflow must represent the relevant logic and be maintained. |
| Runtime-selected, code-first orchestration | Code and agent decisions determine the next task as the work unfolds. | The next useful step depends on what has been learned, and not every branch needs to be fixed in advance. | Adaptability can make behavior less immediately legible than a fully specified path. |
OpenAI’s documentation distinguishes the manager and handoff patterns and recommends keeping specialist jobs narrow. Its practical guide describes code-first orchestration as flexible when the workflow should not require every possible route to be predefined. For graph-based control, Microsoft’s AutoGen documentation describes sequential, parallel, conditional, and looping behavior in GraphFlow, while explicitly marking GraphFlow experimental. That maturity label is important: the presence of documentation does not by itself make a feature production-stable.
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A manager is a good fit when several agents contribute evidence or analysis, but the user needs one coherent response. It can assign bounded work, receive the results, identify gaps or contradictions, and decide what belongs in the final answer. Specialists should return material the manager can evaluate, rather than independently assuming that their own partial response is the finished product.
For example, a workflow answering a policy question might ask one specialist to identify applicable rules and another to find exceptions, then have the manager reconcile those findings. This is an illustrative design, not a claim that a particular framework automatically performs policy review. The important property is explicit final-answer ownership.
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Use a handoff when the specialist should take over
A handoff is appropriate when the next branch is better handled by a specialist that should own the continued interaction or outcome. In the Agents SDK description, a handoff transfers workflow control and the latest conversation state to the receiving agent. This is not a promise that every agent sees all earlier context, or that an application has provided durable memory and every relevant artifact. Define what the receiver needs rather than relying on an assumption of shared state.
Use a graph when path control matters
Choose explicit graph or state-machine control when the sequence itself is part of correctness: a step must wait for prerequisites, a condition determines which branch runs, several branches should fan out, or a cycle has a defined exit. Microsoft’s GraphFlow documentation describes those execution patterns and labels the feature experimental. An explicit graph is useful for visibility into intended transitions; it does not eliminate the need to define task contracts, outputs, or failure behavior.
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Do not create a separate agent for every sub-question by default. OpenAI’s orchestration guide warns that splitting too early can create more prompts, traces, and approval surfaces without necessarily improving the workflow. A specialist is worth introducing when a distinct role materially improves capability isolation, policy isolation, prompt clarity, or trace legibility.
A useful test is whether the subtask has a real boundary. It should need distinct instructions, tools, or policy, or be independently executable in a way that helps the workflow. If two proposed agents need essentially the same context and instructions and would produce overlapping answers, a single agent or a simpler workflow may be easier to understand and operate.
Keep three questions separate when dividing work:
- Can the tasks run independently? If neither task needs the other’s result, parallel execution may be appropriate.
- Does one result enable another task? If so, represent the dependency and sequence the work rather than expecting the second task to infer a prerequisite.
- Who resolves the combined result? Name the manager or final stage responsible for reconciliation and the user-facing answer.
OpenAI’s multi-agent documentation covers delegation, task instructions, concurrency, and coordination events. Parallelism is a scheduling choice for independent tasks, not a substitute for a dependency plan. If one specialist needs another’s finding, passing the result explicitly or arranging an ordered transition is safer than launching both with an unstated expectation.
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Write a task contract and result format
Agents cannot reliably combine work if each task has an ambiguous question or returns a different kind of answer. A task contract says what the agent is being asked to do and what it must return. It should include the goal, relevant context, constraints, and the fields required by the next stage. This makes task boundaries easier to inspect and gives the coordinating agent a concrete basis for deciding whether the result is usable.
Include the minimum useful contract
- Task: one narrow question or action, not a duplicate of the full parent request.
- Context: the facts, prior results, or artifacts needed for this task.
- Constraints: applicable scope, tools, policy limits, and what not to assume.
- Expected result: the answer shape and required details, such as a finding, rationale, unresolved issue, or source reference.
- Completion signal: what counts as done, including how to report that the task cannot be completed from the available information.
For instance, a manager could ask an agent to identify exceptions to a stated rule and return the exception, the condition that triggers it, and any uncertainty. The manager should not ask merely to “look into it” and then expect an easily comparable result. The example is a contract-design illustration; the exact fields should reflect the application.
Make returned data useful to the parent
Choose a stable output shape that serves the next step. A simple illustrative structure could include status, answer, evidence, and open_questions. These are example field names, not a mandated API schema. The point is to preserve the information the parent actually needs: a concise result, its basis, and any limitation that could change how it is used.
For a manager-led workflow, specialists should return results in comparable formats so the manager can evaluate them side by side. For a handoff, the payload must equip the receiving specialist to continue without guessing why the handoff occurred. In both cases, keep the result distinct from the execution signal: an event saying that a subagent was created does not itself contain the analysis that subagent produced.
What context and results actually move between agents
State transfer has more than one meaning. A handoff may transfer the latest conversation state, as described in OpenAI’s practical guide, but that should not be interpreted as a guarantee that every previous message, external artifact, or durable memory is available to every agent. The application needs to decide what information crosses each boundary and where longer-lived information is stored.
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Specify the payload at each boundary. Pass the relevant parent goal and constraints, only the prior findings needed for the receiving task, and any artifact or reference the recipient must inspect. Include enough provenance to tell which result came from which branch. Avoid sending a large undifferentiated history when a concise, explicit handoff can identify the decision, evidence, and unresolved issue.
For serving designs that need durable organization, the Agent Protocol describes concepts including runs, threads, and a store for long-term memory. Those concepts help distinguish a particular workflow execution from its conversational thread and from persistent stored information; they do not mean that all frameworks share one memory model. The application still has to define persistence, access, and retrieval behavior.
In practice, record the results required for synthesis in an explicit result object or durable store, rather than assuming a trace or event stream is the complete record. That distinction becomes especially important when a workflow needs to resume, audit a decision, or investigate a missing answer.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Observability: inspect events, outputs, and gaps
Observability means being able to tell what ran, what decision routed the work, what results came back, and where execution stopped. OpenAI’s multi-agent documentation says coordination events can report subagent creation and coordination actions, but the event stream may omit message content and does not provide a full conversation transcript. Treat those events as operational signals, not as a complete shared record of inter-agent reasoning or output.
A useful operational record separates at least three things: the coordination event, the task’s returned result, and any persistent artifact needed later. With those separated, an operator can answer different questions: Was a specialist launched? Did it return a usable result? Was the result preserved for synthesis or recovery? A trace that proves only the first does not answer the other two.
Decide what should happen when a specialist returns an incomplete result, no result, or an answer that conflicts with another branch. Depending on the workflow, the manager may request clarification, run a bounded follow-up, take a fallback path, or state that the available findings do not settle the question. The appropriate response depends on the application, so it should be designed rather than inferred from the fact that agents are present.
A practical sequence for designing a workflow
- Define the user outcome. State what the completed workflow must deliver, including the form of the final response and any constraints on it.
- Name the owner at each stage. Decide where the manager retains control and where a specialist should receive a handoff and own the next branch.
- Split only at meaningful boundaries. Create a specialist only when its distinct role, instructions, tools, or policy improve the design; give it one narrow task and an expected result.
- Map dependencies. Run independent work in parallel when useful. Sequence dependent work, and make conditional routes or loops explicit when they affect correctness.
- Define context and result payloads. Specify what each agent receives, what fields it returns, and which results or artifacts must be persisted for synthesis or later retrieval.
- Choose the control mechanism. Use dynamic routing where the next step depends on findings; use explicit graph or state-machine structure when ordered transitions, conditions, fan-out, or cycles need to be visible and controlled.
- Instrument the workflow. Capture coordination events and returned outputs separately, and make clear what the event stream does not contain.
- Exercise failure paths. Check what happens with missing, incomplete, or conflicting outputs and whether the workflow has a clear completion condition. These are practical design checks, not reported performance results.
How to decide between dynamic routing and explicit control
The choice is not all-or-nothing. A stable outer workflow can keep accountability, safeguards, and final synthesis explicit, while a bounded inner section allows an agent to select a specialist based on what it discovers. That hybrid is useful when some decisions are predictable but one branch benefits from runtime judgment.
Use runtime routing when the space of possible next tasks is open-ended enough that fixing every path in advance would be awkward. Prefer explicit control when operators need to inspect the route, ordering is non-negotiable, or conditions and exit points must be unambiguous. In either case, constrain the dynamic portion: identify permitted specialists, task boundaries, expected result shapes, and the path back to the owner of the final answer.
When evaluating an implementation, ask who owns the final response; whether routing is selected at runtime or encoded; how dependencies, parallel work, conditions, and loops are represented; what context and artifacts cross boundaries; whether execution can be traced and resumed; and how mature the relevant features are. Microsoft’s experimental label for GraphFlow is a concrete reminder to assess maturity separately from capability.
Common coordination mistakes and how to avoid them
- Agents are added without distinct jobs. Reduce overlap and only split work when the separation improves capability or policy isolation, prompt clarity, or trace legibility.
- The parent expects implicit shared memory. Put required context and artifacts in the task or handoff payload, and define any persistent store separately.
- Independent and dependent work are treated alike. Parallelize independent tasks; sequence tasks that require a prior result or encode the dependency in the workflow.
- Agents return incompatible prose. Give each task a defined result shape with the information the next stage needs.
- Events are mistaken for results. Preserve returned data explicitly because coordination events may not expose message content or a full transcript.
- Dynamic routing has no clear end. Define completion conditions and behavior when the available evidence is insufficient, contradictory, or incomplete.
- Documented behavior is assumed to be mature behavior. Check the feature’s status; GraphFlow is explicitly described as experimental in the cited documentation.
Where the evidence stops
The cited official documentation establishes orchestration patterns, coordination concepts, and documented graph behaviors; it does not establish a universal performance advantage for using multiple agents. No quantified accuracy, speed, cost, or productivity gain follows from these design descriptions. Whether a multi-agent design is worthwhile depends on the task, the quality of its boundaries and state handling, and the operational requirements of the application.
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