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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Make a long-running agent’s work reviewable by keeping an application-level record that connects its task and session IDs to an explicit lifecycle status, progress evidence, outputs, review decisions, and any state needed to resume. OpenAI’s Agents API provides managed sessions, events and items, and artifacts, but it does not prescribe one combined artifact contract. The contract below is an application design pattern, not a built-in API object.
What the Agents API gives you—and what your application must add
The Agents API is an OpenAI-managed harness: OpenAI manages sessions, orchestration, context compaction, and recovery, while your application supplies tools and chooses an execution environment. A documented workflow is to create a session, submit a task, follow progress through a live stream or webhooks, then continue or steer the same session. Agents can work in a sandbox, run code, edit files, connect to MCP servers, and produce artifacts. See the Agents API overview.
Those capabilities provide useful ingredients, but they are not the same as an application’s review record. Your system still needs to define what counts as complete, what a reviewer must inspect, how a pause is represented, and which identifiers connect the work to your own task, storage, and approval systems. Avoid inferring completion merely because text arrived or a session appears idle.
Define a small, durable artifact contract
Keep the record only as large as its consumers require, and use stable identifiers to join application data to the managed session. An illustrative contract might look like this:
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{
"task_id": "app-task-123",
"session_id": "agents-session-456",
"related_work_id": null,
"status": "awaiting_review",
"created_at": "2026-10-04T12:00:00Z",
"updated_at": "2026-10-04T12:03:00Z",
"completed_at": null,
"terminal_reason": null,
"progress_ref": "app://tasks/app-task-123/events",
"final_output": null,
"artifacts": [],
"review_evidence": {
"trace_ref": null,
"tool_call_refs": [],
"validation_results": [],
"approval_decision": null
},
"continuation": {
"interruption_ref": "app://tasks/app-task-123/interruption",
"resumable_state_ref": "app://tasks/app-task-123/state"
},
"environment": "self_hosted_sandbox",
"reviewer_id": null
}This is example application data, not an Agents API request or response shape. Replace the illustrative references with identifiers or secure storage locations your application actually controls. Do not put secrets or sensitive state in a broadly readable review record.
Identity and lifecycle
Store your application’s task ID and the Agents API session ID; add a parent or related-work ID when tasks belong to a larger workflow. Choose explicit statuses that match your application, such as queued, running, awaiting_review, completed, failed, and cancelled. Record created and updated times, a completion time when applicable, and a reason or error for terminal failure. Define allowed transitions so a pause cannot be mistaken for a terminal result.
Progress and evidence
Keep a reference to ordered events or history, or persist compact progress entries that communicate meaningful changes. A reviewer usually needs to understand what happened and when, not necessarily to store every token. Include references to traces, tool-call records, approval decisions, and application validation results where available. Treat absent, unknown, and empty as different states: for example, an empty list of recorded tool calls is not the same assertion as tool-call data being unavailable.
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Outputs and artifacts
When work is complete, store or reference the user-facing final output and the artifacts produced. Record the artifact identifiers and useful metadata such as names and types, plus retrieval references from your application’s storage layer. Keep the session record distinct from the actual file or artifact; a reference is useful only if the right reviewer can resolve it under your access controls.
Continuation and provenance
For a review pause, record what is pending and where the continuation data can be recovered. Keep the eventual approval or rejection and the work resumed afterward tied to the same logical task. Where relevant, record the execution environment, the actor who initiated or reviewed the work, and the retention or deletion handling your application applies.
Represent progress, completion, and interruption separately
The Agents API observability guide describes following a session through live events and saved history, inspecting turns and delegated command execution, and reviewing recorded usage for root-agent and subagent turns. The Platform dashboard supports session inspection; trace export through the public API is available when configured.
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These signals are evidence to interpret, not a reason to overstate what is known. The observability guide notes that usage may be null when unknown and can change; null does not mean zero. It also says the customer API does not indicate whether command output was truncated, so the presence of a command result alone does not establish that the captured output is complete.
When work is waiting for approval
For approval flows documented in the Agents SDK, a paused run may have no final output because it has not finished. The application receives interruptions and resumable state; after a decision, it can resume that state. The SDK’s results and state guide describes result surfaces including finalOutput, history, lastAgent, lastResponseId, interruptions, and resumable state. These are SDK concepts, not a combined Agents API artifact schema.
The Agents SDK running-agents guide likewise treats approval as a paused run rather than a new turn. In your own contract, mark the task incomplete while review is pending, preserve what is needed to resume, and do not publish a partial answer as final simply because a stream stopped.
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Put checks at the side-effect boundary
Approval is not a substitute for validating every consequential action. OpenAI’s guardrails and human review guide distinguishes input guardrails, attached to the first agent; output guardrails, attached to the final-output agent; and tool guardrails, attached to the function tools they protect. If each custom tool call that can create a side effect needs validation, put the check next to each such tool. The API and SDK do not supply your application’s entire policy review.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose a continuation strategy before deciding what to save
The right transcript or state reference depends on how the conversation is continued. The Agents SDK guide describes application-held replay-ready history, SDK sessions, server-managed Conversations API IDs, and Responses API prior-response IDs. It advises using one strategy per conversation unless you deliberately reconcile state: mixing local replay with server-managed state can duplicate context. Agents API sessions are the managed-session path described in the separate Agents API documentation.
Decide which mechanism owns continuation, then store the corresponding stable reference in your application record. A reference to state your system does not retain or cannot access will not make a paused task resumable.
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Compare execution choices against the review requirements
OpenAI describes OpenAI-hosted sandboxes, self-hosted sandboxes, and partner environments as options. The launch announcement names Blaxel, Cloudflare, Daytona, DigitalOcean, E2B, Modal, Oracle, Runloop, and Vercel among ecosystem providers; that establishes their relevance to the ecosystem, not an endorsement or a comparison of their services. Compare the choices using the operational questions below rather than treating the environment name as a review policy.
| Decision axis | Question for your contract and workflow |
|---|---|
| Session state and recovery | Who owns the session reference and recovery path, and which identifiers survive a restart? |
| Pause and resume | Can the task remain incomplete during review, and can the same logical work continue afterward? |
| Progress and traces | Which events, history, and trace data can the reviewer access? |
| Outputs and artifacts | How are produced files or other artifacts identified, retrieved, and permissioned? |
| Execution location | Where do commands run, and which environment is responsible for the execution boundary? |
| Human approval | Which actions require review, who can approve them, and how is the decision recorded? |
| Data controls | What retention, residency, deletion, and access requirements must the deployment meet? |
| Operating cost | Which selected models, OpenAI tools, and sandbox services incur charges for the chosen setup? |
The Agents API launch announcement names partner environments, but it does not make their operational controls interchangeable. Verify the properties that matter for your deployment with the relevant provider.
Account for retention, residency, and cost
In the Agents API overview accessed October 4, 2026, OpenAI says session state is retained so work can continue across turns, customers can delete sessions and published artifacts, data residency is supported only in the United States, and Zero Data Retention (ZDR) is not supported, even with a self-hosted sandbox. Because these controls can change and affect deployment decisions, confirm the current Agents API documentation and applicable data controls before deploying. A self-hosted execution environment does not, by itself, change the stated API data-retention terms.
OpenAI says model usage is billed at selected model API rates, OpenAI tools at their standard rates, and OpenAI-hosted sandboxes at standard container rates. The applicable cost depends on what the application uses; consult current pricing rather than relying on an unqualified figure.
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