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Use an explicit state machine when a workflow needs bounded steps, predictable routing, or clear control over approvals and external actions. Letting an LLM choose what happens next remains useful for open-ended work, and the two approaches can be combined: keep consequential transitions in application code while giving the model discretion inside a defined step.
What changes when workflow control moves into code?
Orchestration is the logic that determines which agents run, in what order, and how the next step is selected. An LLM-led workflow asks the model to choose among next actions. A code-led workflow defines those transitions in the application. OpenAI’s Agents SDK documentation describes both patterns and says they can be mixed.
In an explicit state machine, the application tracks a current state and permits only defined transitions. The model can still interpret input, classify a request, draft a response, or recommend an action; code decides whether the workflow proceeds, pauses for approval, invokes a tool, retries, or ends.
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For example, an illustrative request-processing flow could be:
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- Validate input: reject malformed or incomplete requests, or ask for missing information.
- Classify: use the model to identify the request type and extract relevant details.
- Check policy: have application code evaluate the classification against business rules and required approvals.
- Execute: invoke an external tool only if the workflow’s conditions are met.
- Finalize: record the outcome and return a result, or route the request to a human.
This is an example design, not a report of a tested implementation. Its point is to separate model judgment from control over consequential transitions.
When is code-defined orchestration a better fit?
OpenAI’s Agents SDK guide states: “While orchestrating via LLM is powerful, orchestrating via code makes tasks more deterministic and predictable, in terms of speed, cost and performance.” This is qualitative guidance, not a workload-specific guarantee or a published benchmark. It does not establish that code-defined flow is always more correct or that a state machine prevents errors.
Explicit transitions are especially useful when the workflow has required stages, must stop for approval, or can trigger meaningful external effects. They make it easier for a team to inspect which conditions allow an action and who owns each decision. The tradeoff is that developers must define and maintain those transitions as requirements change.
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- Keep model-led choices where the next step genuinely depends on flexible interpretation and the possible actions are appropriately constrained.
- Mix both when the model is valuable for reasoning within a step, but the application should control which steps are permitted.
Who owns the agent loop, state, and approvals?
A state machine is an application architecture choice; it is not a particular SDK or hosted service. OpenAI’s Agents SDK overview says the SDK runs in the application: the application owns deployment, tool implementations, state storage, and approval decisions, while the SDK runs the agent loop and invokes tools. The overview contrasts that arrangement with the managed Agents API and direct use of the Responses API, which assign orchestration and state responsibilities differently.
Choose the runtime arrangement by deciding explicitly where workflow state lives, who can approve an action, and which component may call each tool. Do not assume that adopting an SDK automatically supplies application-specific persistence, approval policy, or recovery behavior.
How should a workflow handle tools and side effects?
A check that blocks a result is not the same as preventing an action. OpenAI’s Agents SDK guardrails guide explains that guardrails can run alongside agents or block execution until checks complete. It also warns that a guardrail trip does not undo external side effects that already occurred, retract output already delivered to application code, erase data stored outside SDK control, or modify application-owned references to raw provider data.
For consequential tools, put authorization and any necessary checks before the call that changes an external system. If an action may be repeated after a timeout or retry, design the application’s recovery behavior around that side effect rather than assuming a guardrail will reverse it.
Routing also affects control. The Agents guide distinguishes a manager that retains control and calls specialists as tools from a handoff that passes control to a specialist. A manager can centralize guardrails or rate limits; a handoff lets the specialist take over the task. Which arrangement fits depends on who should retain routing and policy control.
What changes when workflows wait, retry, or restart?
A process-local state machine may not be enough when runs need to survive long waits, retries, or process restarts. OpenAI’s runtime guide identifies durable-orchestration integrations, including Temporal and Restate, as options to consider for those needs. The documentation does not provide a comparative evaluation establishing which integration fits a particular workload.
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Before choosing a durability approach, decide what must persist across a restart: the current state, model and tool results, pending approvals, and whether an external action has already happened. The recovery design should account for those facts so a resumed workflow does not blindly repeat a consequential operation.
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Compare the options against the workflow you actually need to run, rather than treating either “agent” or “state machine” as a universal answer:
- Next-step selection: Does application code need to decide every transition, or can the model choose among constrained actions?
- Boundaries: Is the workflow a known sequence with explicit stop conditions, or is it exploratory and open-ended?
- Ownership: Which component stores state, approves actions, and controls tool access?
- Recovery: Must a run survive waits, retries, or process restarts?
- Side effects: Where are external changes made, and what happens if execution fails around a tool call?
- Maintenance: Can the team afford to keep explicit states and transitions aligned with changing requirements?
Choose code-defined transitions for the parts where control and operational predictability matter. Let the model handle the interpretation that benefits from language-model flexibility, and make the boundary between its judgment and application authority explicit.
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