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To make a multi-agent workflow predictable, put its routing, step order, validation, retry limits, and stop conditions in application code. Let models handle bounded tasks that need judgment, but treat their outputs as variable inputs—not as deterministic parts of the system. This guide shows how to define that control boundary, choose between handoffs and manager-led calls, and persist runs so they can be inspected or resumed.
What “deterministic” means in a multi-agent workflow
There are two broad ways to choose what happens next: let a model decide the workflow steps, or have application code determine the flow. The OpenAI Agents SDK’s orchestration guide describes code orchestration as making tasks more deterministic and predictable in speed, cost, and performance. That is a claim about workflow behavior—not a guarantee that model reasoning, model responses, or tool results will be identical on every run.
A practical design draws a line between the two:
- Application-owned: required stages, permitted transitions, which agent is called, output validation, retry caps, approval pauses, persistence, and terminal outcomes.
- Model-assisted: bounded work such as classifying a request, summarizing material, or drafting a response.
For example, code can always require intake, research, and review, while a model supplies the research findings or draft. Code then checks those results before allowing the workflow to advance. The model is part of the process, but it does not own the process.
Define state and legal transitions first
Start with the states your application recognizes and the events allowed to move between them. The following small TypeScript example makes an intake → research → review → done path explicit, with an approval pause and bounded research retries. It is an illustrative application pattern, not code from an SDK integration.
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type State =
| { stage: "intake"; request: string }
| { stage: "research"; request: string; attempt: number }
| { stage: "review"; request: string; findings: string }
| { stage: "awaiting_approval"; request: string; draft: string }
| { stage: "done"; answer: string }
| { stage: "failed"; request: string; reason: string };
type Event =
| { type: "start_research" }
| { type: "research_complete"; findings: string }
| { type: "research_invalid"; reason: string }
| { type: "review_passed"; answer: string }
| { type: "approval_required"; draft: string }
| { type: "approval_granted"; answer: string }
| { type: "approval_rejected"; reason: string };
const MAX_RESEARCH_ATTEMPTS = 2;
function transition(state: State, event: Event): State {
switch (state.stage) {
case "intake":
if (event.type === "start_research") {
return { stage: "research", request: state.request, attempt: 1 };
}
break;
case "research":
if (event.type === "research_complete") {
return {
stage: "review",
request: state.request,
findings: event.findings,
};
}
if (event.type === "research_invalid") {
if (state.attempt < MAX_RESEARCH_ATTEMPTS) {
return { ...state, attempt: state.attempt + 1 };
}
return { stage: "failed", request: state.request, reason: event.reason };
}
break;
case "review":
if (event.type === "review_passed") {
return { stage: "done", answer: event.answer };
}
if (event.type === "approval_required") {
return {
stage: "awaiting_approval",
request: state.request,
draft: event.draft,
};
}
break;
case "awaiting_approval":
if (event.type === "approval_granted") {
return { stage: "done", answer: event.answer };
}
if (event.type === "approval_rejected") {
return { stage: "failed", request: state.request, reason: event.reason };
}
break;
case "done":
case "failed":
break;
}
throw new Error(`Illegal transition: ${state.stage} + ${event.type}`);
}The state union makes stage-specific data visible to TypeScript, and the transition function rejects events that do not belong to the current stage. This does not validate persisted JSON or an agent response at runtime: parse and validate both at their boundaries before calling transition. A production workflow should also define what timeout, cancellation, and human approval mean for its own use case rather than allowing an unfinished run to look like success.
Notice that a retry is still an explicit transition. The counter is persisted with the research state, so a restart does not silently reset the limit. The application must also decide whether a retry repeats the same call, changes the prompt, or routes to another specialist; that choice is part of the workflow contract.
Keep agent calls behind narrow contracts
Give each agent one bounded job and a defined input/output shape. For a research step, for instance, the application can pass a request and expect a structured findings object. Validate that result before converting it into a research_complete event. If it is malformed, emit a failure or retry event instead of letting invalid data change workflow state.
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Use code to select required stages and route structured classifications. Use a model where judgment is needed, then record the decision, validated output, and provenance needed to inspect or resume the run. The Agents SDK orchestration guide discusses structured outputs as a way for code to inspect data before selecting the next agent; structured output still needs application-side validation and does not make model reasoning deterministic.
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Introduce a specialist when it materially improves capability, policy isolation, prompt clarity, or trace legibility. Splitting one job into several agents without a clear benefit adds prompts, transitions, traces, and possibly approval points to maintain.
Choose who owns each branch
The key distinction is not simply how many agents are involved. It is who is responsible for the branch’s result and the final response. The OpenAI guidance on orchestration and handoffs describes both patterns and notes that they can be combined.
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| Pattern | Who owns the result? | Good fit |
|---|---|---|
| Handoff | The specialist takes control and is responsible for the response. | A branch where the specialist should handle the conversation or task directly. |
| Agent as a tool | A manager remains responsible for the final response and calls a specialist for a bounded job. | Tasks such as classification or summarization that feed a manager’s synthesis. |
Use a handoff when the specialist should own what happens next. Keep a manager in charge when it should combine specialist results, apply a common response policy, or decide whether the workflow advances. Make routing descriptions concrete enough that the system’s branch ownership is unambiguous.
Choose one continuation model for each conversation
Continuation determines where prior context lives and what your application must save. The OpenAI guide to running agents describes several approaches. Choose one deliberately for a conversation unless your application has an explicit reconciliation design; mixing local history with server-managed context can duplicate or conflict with prior state.
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|---|---|---|
| Application-managed replay history | Your application retains and resends the relevant history. | You need maximum control over what context is carried forward. |
| SDK session | A session backed by your storage. | You want resumable state held in storage under your application’s control. |
| Conversations API conversation ID | Server-managed conversation state identified by a conversation ID. | Services need to share server-managed conversation context. |
| Responses API previous-response ID | A response-to-response continuation reference. | You need a lighter continuation link between responses. |
Conversation history is not necessarily the same thing as workflow state. Persist the stage, attempt count, approval status, and validated results your application needs to resume or explain a run, even when a service retains conversation context. Save checkpoints at boundaries where the application can safely continue.
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Know when in-process execution is not enough
A basic agent run can loop through model calls, tool calls, and handoffs until it reaches a stopping point. That may be suitable when the work is short-lived and the application can handle interruption itself. For work that must survive a worker restart, recovery needs to be part of the execution architecture rather than an assumption that the process will remain alive.
Temporal’s TypeScript integration guide for the OpenAI Agents SDK describes putting orchestration in a Workflow and model calls in Activities. It says model calls retry durably and are not repeated during workflow replay. This is a documented integration pattern, not a benchmark or a claim that it is necessary for every agent application.
Consider a durable workflow engine when a run is long-lived, must recover across worker restarts, or needs durable retries. For a short request/response loop, the extra workflow infrastructure may not be justified. Decide based on recovery requirements, persistence needs, and operational complexity.
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Make transitions observable and testable
Record enough information to answer what happened, why a branch was selected, and whether the run can resume—while applying appropriate privacy and retention controls to prompts and outputs. A useful transition record can include:
- Run and transition identifiers, prior stage, emitted event, and resulting stage.
- The routing decision and the validated result that informed it.
- Tool calls, handoffs, validation failures, retry count, and approval pauses.
- The terminal reason: completed, rejected, exhausted retries, or runtime failure.
Build evaluations around workflow behavior as well as answer quality. Include expected routes, malformed agent output, repeated or looping transitions, approval pauses, and recovery after interruption. The Agents SDK orchestration guide recommends monitoring, iteration, and investment in evaluations. These checks help reveal whether the application’s control flow behaves as designed; they do not prove that model outputs will be repeatable.
Pick frameworks to match the control problem
Framework choice should follow requirements for control, persistence, latency, customization, deployment, and complexity—not an assumed performance ranking. The official documentation covered here does not establish an across-framework performance winner.
| Option | What the documentation supports | Fit to consider |
|---|---|---|
| Application-owned TypeScript state machine | The explicit state and transition pattern in this article is a design recommendation synthesized from the orchestration and runtime guidance, not a framework feature claim. | When the workflow should have a small, directly inspectable set of states and transitions. |
| OpenAI Agents SDK | Its guides cover code versus model orchestration, handoffs, agents called as tools, run continuation, sessions, pauses, and failures. | When using that SDK and deciding how its agent orchestration and continuation fit the application. |
| LangGraph | The LangGraph reference positions it as a low-level framework for long-running, stateful agents and points JavaScript/TypeScript users to LangGraph.js. The reference URL redirected when reviewed, so check the current JavaScript documentation before relying on implementation-specific details. | When advanced needs combine deterministic and agentic workflows with customization and carefully controlled latency. |
| Temporal TypeScript integration | The integration guide documents Workflow/Activity orchestration and durable retry/replay behavior for model calls. | When work needs durable execution across worker restarts. |
Do not add a framework just to make a workflow look more agentic. Each additional prompt, state boundary, trace, and approval surface has a maintenance cost; adopt those boundaries when they improve control or capability for a real requirement.
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