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No. A healthy oracle sample says that a monitored signal looked healthy at a particular moment; it does not prove that an AI agent’s run state, session history, pending actions, approvals, or workspace are safe to continue. Use health as one input to a guarded recovery decision, not as automatic authorization to resume.
What a healthy sample does—and does not—tell you
An oracle sample is evidence about the signal it measures, at the time it was collected. Its scope might be a process, environment, or other monitored component. It cannot establish by itself that the agent’s durable run state is valid or that work which produced external effects has not already completed.
Recovery can involve several distinct state surfaces: the orchestration harness’s run state, provider-side session history, and the workspace or sandbox. A component may be reachable while one of those surfaces is missing, stale, or inconsistent. The OpenAI sandbox guidance distinguishes these surfaces; its lifecycle guidance also makes clear that reconnecting does not restore files, and pending-input recovery after a process crash is not guaranteed.
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Use a supported checkpoint as the resume boundary
Resume from a durable state representation that the framework explicitly supports, rather than inferring resumability from a live connection or heartbeat. In the OpenAI Agents SDK, RunState is a serializable pause-and-resume boundary for interruption flows. Its results guidance describes how resumed work can reconcile persisted session history and identifies cases where state is unrecoverable.
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Checkpointing is useful, but it is not a safety guarantee on its own. AWS recommends persisting checkpoints at natural workflow boundaries and making steps idempotent; Microsoft’s workflow documentation describes checkpoints at the end of a workflow superstep and storage options. These are patterns to evaluate against the framework and workload, not proof that every resumed workflow is safe.
Check state before deciding to resume
- Identify the signal. Establish exactly what the health sample measures, which component it covers, and when it was produced. A healthy result for one component does not certify the whole run.
- Read authoritative run and session state. Inspect the task or run lifecycle status rather than inferring it from health. If the substrate and workflow disagree, reread authoritative state before taking a terminal or resume action. An AWS sample orchestrator illustrates state-aware health interpretation and a second state read before declaring a task failed; its mechanics are specific to that sample.
- Load and validate the checkpoint. Confirm that the supported RunState or checkpoint belongs to the expected run, has a compatible schema, and remains eligible for resumption. Preserve approval decisions, guardrail results, and turn budgets where the framework requires them.
- Reconcile history and side effects. Determine which tool calls and external actions have already completed. Use idempotency keys or application-level deduplication for effect-bearing steps. If session history is ambiguous, stop rather than replaying work that may have succeeded.
- Verify the workspace. Confirm that the execution environment and required files have been restored or reattached. A healthy connection or environment identifier alone does not restore workspace contents.
- Resume only if every required check passes. If state is ambiguous, terminal, or marked unrecoverable, fail closed. Repair original history where the framework supports it, or start a new run according to its documented lifecycle.
Prevent duplicated actions when work is retried
A resumed agent may encounter a tool call whose external effect completed even though the run did not record a clean completion. Retrying that call can duplicate an order, message, update, or other action. AWS’s Well-Architected Agentic AI Lens recommends combining checkpoints with idempotent workflow steps; it warns that checkpoints without idempotency can lead to duplicate side effects or data corruption.
The Agents SDK documents a more specific safeguard: a resumed run can reconcile an exact pending session batch before making another model call, avoiding reruns of completed tools. If session history does not match or is ambiguous, the SDK raises an error rather than assuming it is safe to proceed. These are SDK-specific behaviors, so verify the semantics for the framework and version in use.
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Revalidate approvals and guardrails
Human approval is part of the run’s state, not a substitute for checking it. In an Agents SDK human-in-the-loop flow, the run resumes from its original state after the approval decision. Tool input guardrails may run again after approval because the call could have become unsafe while it was waiting. Preserve and validate the approval state and the applicable guardrail outcomes before execution; do not treat an old approval as blanket authorization for changed inputs or circumstances.
Separate reconnection from restoration
Reconnecting to a sandbox or process answers whether a connection can be re-established; it does not show that a killed command restarted, that files returned, or that pending input was recovered. Check the session or turn outcome before retrying, and use persisted storage or snapshots when workspace restoration is required. Keep environment recovery distinct from session-history and run-state recovery.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When finalization makes a run unrecoverable
Not every saved state is resumable. The Agents SDK results guidance describes a boundary where final output and terminal hooks have been accepted but persistence fails. In that case, replaying the run could repeat terminal lifecycle effects, so the state is treated as unrecoverable. Follow the framework’s lifecycle guidance instead of forcing a resume based on a healthy signal.
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How to evaluate a recovery design
- Signal quality: Is the sample fresh, and what component does it actually cover?
- State authority: Does the system reconcile health with authoritative task, run, and session state?
- Checkpoint validity: Is the checkpoint durable, tied to the correct run, and compatible with the current schema or version?
- Side-effect safety: Can the system detect completed actions and deduplicate retries?
- Workflow controls: Are approvals, guardrails, and turn limits preserved and checked on resume?
- Environment recovery: Are workspace files and execution resources restored independently of connectivity?
- Ambiguous outcomes: Does the system stop safely when history is inconsistent or final output was accepted but persistence failed?
These questions expose why a single health signal cannot serve as a universal resume switch. Recovery depends on the interaction among signal scope, durable run state, history, side effects, controls, and the execution environment.
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Do not copy sample heartbeat thresholds as defaults
The AWS sample orchestrator uses a 45-second heartbeat write interval, a 120-second startup grace period, and a 240-second stale threshold. Those are configuration values in that sample repository, not general reliability statistics or universal recommendations. Choose and validate thresholds for the workload, failure modes, and monitoring system rather than treating them as standard settings.
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