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To make an AI agent available around the clock, build a continuously available application that starts agent runs when a schedule or event calls for them, saves the state needed to continue work, and can recover from failures. An agent SDK runs the agent loop; it does not, by itself, keep a service online or guarantee uninterrupted uptime.
What “24/7” means for an AI agent
A continuously available agent is a service that can accept or detect work at any time and launch bounded runs to handle it. A run may finish, pause for human input, or fail and need a retry. The service—not one never-ending model call—coordinates those runs and their lifecycle.
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OpenAI’s Agents SDK documentation describes the SDK as the agent loop running within an application. It puts deployment, tool implementations, state storage, and approval decisions under the application owner’s control. In the documentation’s words: “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.” That flexibility also leaves the operational work with the team building the service.
How the pieces fit together
A practical architecture separates the trigger, the agent run, and the recovery path. Each component has a distinct job:
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- Trigger: A scheduler starts recurring work, or an event source starts a run when something happens.
- Application runtime: The deployed application receives the trigger and launches the agent. It must be able to run again after a process restart.
- Agent loop and tools: The SDK runs the agent and invokes the tools the application makes available. Give those tools only the access and authority needed for their tasks.
- Persistent state: Store the information required to continue work across runs or workers. Do not assume in-memory state will survive a restart.
- Observability: Record run outcomes and expose failures through logs, traces, and alerts so someone can tell whether work is succeeding.
- Retry, recovery, or review: Retry recoverable failures safely, resume long-running work where appropriate, and route actions requiring judgment to a person.
This design keeps availability separate from execution: the application can remain ready to start work even when no agent run is active.
Choose who operates the runtime
Runtime choice is chiefly a decision about control and operational responsibility. The Agents SDK documentation describes an application-owned approach and distinguishes it from managed-runtime options. It does not establish that one approach suits every deployment.
| Decision area | Application-managed SDK | Managed runtime |
|---|---|---|
| Deployment | Your application owner manages deployment and execution. | Evaluate what the runtime provider operates and what remains your responsibility. |
| Tools and integrations | Your application owns tool implementations and their integration. | Check how the runtime connects to the tools and systems your agent needs. |
| State | Your application chooses and manages state storage. | Check where state is stored, how long it persists, and whether it can be shared across runs or workers. |
| Approvals | Your application owner controls approval decisions. | Confirm how human approval is represented and resumed in the runtime. |
| Operational fit | Offers control over the application’s deployment and implementation, with corresponding operational work. | Assess the runtime’s deployment model, integration effort, and fit with your reliability requirements. |
Use the application-managed SDK when you need to own those decisions and can operate the service around it. Consider a managed runtime when its operating model fits your deployment and integration requirements. In either case, verify the actual responsibilities and state behavior for the option you select; the SDK documentation does not supply a universal uptime guarantee.
Design state for work that spans runs
State is not one interchangeable bucket. The SDK run guide describes session and conversation state as separate mechanisms, so select the one that matches what must persist and who needs to access it. A value that must be available to another worker or a later run needs storage and sharing behavior appropriate to that use; process memory alone is not enough.
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- Identify what the agent must remember between steps, runs, and restarts.
- Choose whether that information belongs to a session or conversation, and determine whether one worker or multiple workers need access.
- Persist progress before a long wait or a side effect that should not be repeated blindly.
- Define how the application detects that saved progress is stale, incomplete, or no longer safe to resume.
State design should match the work, not merely the SDK API: a short, independent task may need little cross-run persistence, while a process involving a long wait or human review needs a deliberate resume path.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When to add durable workflow execution
Ordinary application execution may be enough for short runs that can safely start over. Consider durable workflow execution when a job can wait a long time, require human review, encounter retries, or need to continue after a process restart or failure. The Python SDK guide names Dapr, Temporal, Restate, and DBOS as durable workflow integrations. It presents them as options, not mandatory components for every agent.
The available documentation does not establish a complete side-by-side feature or pricing comparison among those integrations. Evaluate each candidate against the needs of your own workflow rather than assuming that the names imply identical recovery behavior.
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- Long waits: How does the workflow pause and continue when waiting on an external event or a person?
- Human approval: Can the workflow stop for a decision and continue from the approved point?
- Deployment: What must your team operate, and how does the option fit your existing language and runtime?
- State: How are workflow progress and the agent’s session or conversation state stored and accessed?
A durable workflow layer can address long-running execution and recovery patterns; it does not replace careful tool permissions, monitoring, or application-level decisions about which actions require approval.
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Make retries safe and failures visible
A retry can repeat an action that already succeeded if the application lost the response or failed before recording completion. For any tool that changes external state, decide how the application will detect duplicates or make repeated attempts safe. Record enough progress to distinguish “not attempted,” “completed,” and “outcome uncertain,” and route uncertain consequential actions for review rather than blindly repeating them.
Monitor the service as well as individual agent runs. Useful signals include whether triggers are being received, whether runs finish or fail, and whether a failure remains unresolved. Set alerts that lead to an actionable response, and define who investigates or takes over when automated recovery is not appropriate.
Production-readiness checklist
- Secrets and access: Keep credentials out of prompts and logs; scope credentials and tool permissions to the work each agent needs to do.
- State durability: Decide what must survive a run, worker change, or restart, and verify that the selected storage and sharing model supports it.
- Retries and side effects: Set retry limits and a strategy for avoiding duplicate changes or escalating uncertain outcomes.
- Health and alerting: Observe trigger handling and run outcomes, and alert a responsible person when failures need intervention.
- Evaluation and guardrails: Evaluate agent behavior against the tasks it is meant to perform, and constrain what it may do through tools.
- Human oversight: Define which actions require approval and provide a clear escalation path for failures or consequential decisions.
- Restart recovery: Test how the application resumes, retries, or safely abandons work after a process stops.
Framework choice is only one part of reliability. The application’s deployment, state, recovery behavior, monitoring, and oversight determine whether it can keep handling work over time.
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