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To make an AI agent work 24/7, build a continuously available application that starts separate agent runs in response to schedules or events, saves the state each run needs, and can recover from failures. An agent SDK runs the agent loop; by itself, it does not keep a service online or guarantee uninterrupted availability.
What “24/7” means for an AI agent
A reliable agent service does not depend on one model call or process running forever. Instead, the application stays available to accept work, starts bounded agent runs when needed, records progress, and handles failures or requests for human judgment. A run may end while the service remains ready to start another.
The distinction matters because an SDK is one component of the application. The OpenAI Agents SDK documentation describes the SDK track as one where the application’s server owns deployment, tool implementations, state storage, and approval decisions, while the SDK runs the agent loop and invokes those tools. An organization choosing that approach also takes responsibility for operating those surrounding systems.
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How the pieces fit together
Think of continuous operation as a path through the system rather than a single “always-on” agent:
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- Trigger: A schedule, incoming event, or user action creates a unit of work.
- Application runtime: A deployed service or managed runtime accepts the trigger and starts an agent run.
- Agent and tools: The agent reasons over the task and invokes only the tools the application makes available.
- Persistent state: The application stores the information needed to continue or coordinate work across runs and workers.
- Observability: Logs, traces, and alerts help operators see whether work started, progressed, failed, or needs attention.
- Recovery or review: The system retries eligible work, resumes durable work where supported, or sends consequential decisions to a person.
This separates availability from execution. A process can restart and a run can fail without requiring the whole service to be designed around one uninterrupted agent session.
Choose who operates the runtime
Decide whether your application will own the agent’s execution environment or whether a managed runtime better fits your operational needs. The choice affects control and responsibility; it is not a guarantee of uptime either way.
| Question | Application-managed SDK | Managed runtime |
|---|---|---|
| Who deploys and operates the execution environment? | Your application team owns deployment and operation. | Depends on the runtime and its service boundary; confirm what the provider operates. |
| Who implements tools and controls access? | Your application team implements the tools and decides what the agent can invoke. | Confirm how tools are connected, permissioned, and constrained. |
| Who owns state and approvals? | Your application team chooses state storage and approval decisions. | Confirm where state lives, how it is shared, and how approval steps work. |
| What should drive the decision? | Choose this when you need control over deployment, tools, storage, and approval logic and can operate them. | Evaluate the execution environment, integration effort, state handling, approvals, and operational responsibilities against your requirements. |
The OpenAI Agents SDK documentation characterizes its SDK track this way: “Use the SDK track when your server owns deployment, tool implementations, state storage, and approval decisions, while the SDK runs the agent loop and invokes those tools.” Assess the actual runtime documentation for any managed option you consider; capabilities and responsibilities vary, and the available documentation does not establish a universal winner.
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Design state for work that spans runs
State is not automatic simply because an agent uses a session or conversation. The Agents SDK run guide describes session and conversation state as separate mechanisms. Choose deliberately based on what your application must preserve and whether that information must be shared between runs or workers.
Separate durable task data from conversational context
For each task, decide which information must survive a process restart, which workers need access to it, and how long it should be retained. Store the task’s progress and business-critical results somewhere your application can retrieve after a run ends. Treat conversation history as a distinct concern: retain or share it only where the application’s behavior requires it.
Make progress and side effects recoverable
Record enough progress to tell what has completed and what remains. Before retrying a tool action that changes something outside the agent—such as creating a record or sending a request—design for duplicate attempts. Use an idempotency mechanism where the target system supports one, or check the current result before repeating the action. This reduces the risk that recovery turns one intended action into several.
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When to add durable workflow execution
A basic application-managed run can suit short, bounded tasks. Consider a durable workflow integration when a task may wait a long time, require human review, encounter process restarts, or need recovery after failure. The Python Agents SDK guide names Dapr, Temporal, Restate, and DBOS integrations for durable workflows. It does not say that every agent needs one or provide a fair feature or pricing ranking among them.
Evaluate a candidate against the failure cases your workflow actually has:
- Can work resume after the process that started it stops?
- How are long waits and retryable failures represented and handled?
- How does a human approval pause and later continue the workflow?
- Who deploys and operates the workflow runtime?
- How does workflow state relate to the application’s session and conversation state?
- What mechanisms help prevent repeated external side effects during retries?
Answer these questions from the documentation for the specific integration and version you plan to use. The integration names alone do not establish which one best fits your language, deployment model, or recovery requirements.
Build for failures, not an unbroken process
Bound each run
Give each unit of work a clear start, completion condition, and failure outcome. Avoid treating an agent loop as an indefinitely running task. If it cannot finish immediately, persist progress and arrange for a later event or worker to continue it.
Make retries intentional
Classify failures before retrying. A temporary problem may merit another attempt; an invalid request or a tool action with an uncertain outcome may require inspection or human review instead. Set retry behavior at the application or workflow level, and ensure it cannot silently repeat consequential actions.
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Monitor whether triggers are accepted, runs start and finish, tools fail, work remains pending, and approvals are waiting. Route actionable failures to an alert or queue that someone owns. Observability helps identify and respond to incidents; it does not itself prevent downtime.
Production checklist
- Runtime: Choose an application-managed or managed execution environment, and identify who operates each part.
- Triggers: Specify how scheduled or event-driven work enters the service and what happens if a trigger is delayed or duplicated.
- Secrets and permissions: Keep credentials out of prompts and limit each tool to the access it needs.
- State: Decide what persists across runs, where it is stored, and which workers can access it.
- Recovery: Define retry rules, duplicate-action protections, and whether long-running work needs durable execution.
- Monitoring: Track run outcomes and tool errors, and assign responsibility for alerts and stuck work.
- Evaluation and guardrails: Evaluate behavior against representative tasks and constrain actions that could cause harm.
- Human oversight: Set an approval or escalation path for consequential actions and cases the agent cannot safely resolve.
The Agents SDK deployment guidance treats observability, evaluation, guardrails, and human oversight as production concerns. Treat continuity as an end-to-end engineering property of your application and its dependencies—not as a promise attached to a framework choice.
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