To make long-running work reviewable, give each logical task a durable application ID and record its lifecycle state, progress evidence, outputs, review decisions, and continuation data together. OpenAI’s Agents API provides managed sessions, events and artifacts, but it does not prescribe one combined artifact contract. The record below is an application-level design, not a built-in API object.
What a reviewable run needs to show
A reviewer should be able to answer four questions without guessing: Which task is this? Is it still running, finished, failed, or waiting for a decision? What evidence supports that status? If it is paused, what is needed to continue it safely?
As an Amazon Associate I earn from qualifying purchases.
OpenAI describes the Agents API as a managed Codex harness: OpenAI manages sessions, orchestration, context compaction, and recovery, while the application supplies tools and chooses the execution environment. A documented workflow is to create a session, submit work, follow progress through streaming or webhooks, then continue or steer that session. Agents can use a sandbox, run code, edit files, connect to MCP servers, and produce artifacts. See the Agents API overview.
The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Keep the API distinct from the Agents SDK. The API overview describes managed sessions, events or items, and artifacts. Properties such as finalOutput, history, lastAgent, lastResponseId, interruptions, and resumable state are SDK result surfaces, not fields to assume exist on an Agents API object. They can inform application design, but retain that distinction when implementing the contract. The SDK’s results and state guide and running agents guide explain those SDK surfaces.
#1 Best Overall
Define the application-level artifact contract
Use a small record whose fields each serve a real reader or system: the UI, an approval workflow, an auditor, or a recovery worker. Prefer stable IDs and explicit state transitions; do not infer completion merely because text arrived or a session appears idle.
- Identity: An application task ID, the Agents API session ID, and a parent or related-work ID when the task belongs to a larger workflow. Keep the application ID stable even if you add separate attempts or child tasks.
- Lifecycle: A status such as
queued,running,awaiting_review,completed,failed, orcancelled, plus created and updated timestamps and a completion timestamp when applicable. Store a failure reason for terminal failures. Define allowed transitions in your application rather than deriving them from incidental event text. - Progress: A reference to ordered session events or history, or a compact application-maintained progress log. Record meaningful transitions—such as work starting, a tool action being requested, a review being required, or work resuming—without assuming every token or intermediate detail is persisted.
- Outputs: The final user-facing result only when complete, along with artifact IDs, names, types, and retrieval references from your storage layer. Keep output availability separate from run status: an intermediate file does not itself mean the task is complete.
- Review evidence: References to traces, tool-call records, approval decisions, and application validation results where available. Distinguish absent (not recorded), unknown (not determined), and empty (checked and contains no entries). These states have different meanings during review.
- Continuation: For a paused run, the pending interruption details and a reference to the state needed to resume it. Attach the approval or rejection and subsequent resumed work to the same logical task so the audit trail does not break at the decision boundary.
- Provenance and access: Record the execution environment and, where relevant, the initiating actor and reviewer. Apply the retention, deletion, and access rules required by your application.
One conceptual record could look like this; the field names and values are examples for an application to adapt, not an OpenAI schema:
{
"task_id": "task_123",
"session_id": "session_456",
"status": "awaiting_review",
"created_at": "…",
"updated_at": "…",
"progress_ref": "…",
"outputs": [],
"review": {
"pending": true,
"evidence_refs": ["…"],
"decision": null
},
"continuation_ref": "…",
"environment": "…"
}Represent unknown values deliberately instead of silently turning them into zero, an empty list, or a successful result. Keep the resumable state in the continuation mechanism you have selected, and let the contract point to it rather than copying opaque state into multiple records.
Track progress, completion, and failure with evidence
The Agents API observability guide describes following a session through a live event stream 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. Use the observability and usage guide to determine which evidence surface fits the task.
- For a live view, consume progress updates but persist the identifiers and state your own application needs to reconcile the stream with saved history.
- For post-run review, retain references to relevant events, command executions, traces, artifacts, and validation outcomes rather than relying on a final prose answer as the only record.
- For failures, preserve the terminal reason and the evidence available at that point. If recovery creates a continuation or another attempt, link it to the same logical task while distinguishing attempts.
Two observability caveats matter when presenting evidence. Usage may be null when unknown and may change; null is not zero. The customer API also does not indicate whether command output was truncated, so the presence of a command result does not prove that the complete output is available.
Model approval as a pause, not a finished answer
For SDK approval flows, OpenAI’s results guide says a paused run may have no final output because it has not finished. The application receives interruptions and resumable state; after a decision, it resumes that same state. The running-agents guide likewise treats approval as a paused run rather than a new turn. In your contract, awaiting_review should therefore be incomplete: preserve the interruption, the evidence presented to the reviewer, the decision and reviewer identity, and the reference needed to continue. Do not publish a partial answer as final just because the stream paused. These property-level details describe the SDK guides; use the Agents API’s documented session and event surfaces for API-specific implementation.
Place checks at the boundary that needs them. OpenAI’s guardrails and human review guide distinguishes input guardrails (first agent), output guardrails (final-output agent), and tool guardrails (the function tools they are attached to). If every side-effecting custom tool call requires validation, attach a check to each tool that can create that side effect. Neither an API nor an SDK supplies the application’s entire policy review automatically.
Free tools Windows power users keep installed
One-click scans. No signup required.
Choose the continuation mechanism before designing storage
The Agents SDK guide describes several ways to carry conversation state. Choose deliberately: its guidance is to use one strategy per conversation unless you are intentionally reconciling state, because combining local replay with server-managed state can duplicate context. Agents API sessions are the managed-session option documented separately in the Agents API overview.
| State approach | What your contract should reference | Documentation context |
|---|---|---|
| Application-held replay-ready history | The application’s history reference and the identifiers needed to replay it | Agents SDK guide |
| SDK sessions | The SDK session reference used to continue that conversation | Agents SDK guide |
| Server-managed Conversations API | The conversation ID | Agents SDK guide |
| Responses API prior response | The prior response ID | Agents SDK guide |
| Agents API managed session | The Agents API session ID, plus application task identity and related review/output references | Agents API overview |
This is a storage-design comparison, not a claim that the approaches have identical behavior or are interchangeable. Keep your contract explicit about which continuation path a task uses; if you deliberately combine mechanisms, define how duplicate or conflicting history is reconciled.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Account for retention, residency, and environment choice
As described in the Agents API overview accessed October 4, 2026, the service retains session state so work can continue across turns, lets customers delete sessions and published artifacts, supports data residency only in the United States, and does not support Zero Data Retention (ZDR), including with a self-hosted sandbox. These terms are consequential and may change, so verify the current Agents API documentation and applicable data controls before deployment or a policy decision.
OpenAI lists OpenAI-hosted sandboxes, self-hosted sandboxes, and partner environments as execution options. The launch announcement names Blaxel, Cloudflare, Daytona, DigitalOcean, E2B, Modal, Oracle, Runloop, and Vercel in the ecosystem. That establishes integration relevance, not an endorsement or a comparison of providers. Choose an environment against your own requirements for execution control, approval boundaries, access, retention, and operating cost. The overview says model usage is billed at selected model API rates, OpenAI tools at their standard rates, and OpenAI-hosted sandboxes at standard container rates; consult current pricing rather than relying on an assumed fixed figure. See OpenAI’s Agents API announcement.
Quick wins for a faster PC:
Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Implementation sequence
- Choose state ownership. Decide whether this workflow uses an Agents API managed session or a different documented continuation strategy. Record that choice alongside the task and session identity.
- Define state transitions. Specify which application events move a task from queued to running, awaiting review, completed, failed, or cancelled. Require explicit evidence for completion.
- Connect progress and evidence. Persist references to the event stream or saved history, applicable traces and tool records, artifacts, and validation results. Distinguish unknown data from verified empty data.
- Implement the review boundary. When approval is required, store the pending interruption and evidence, collect a decision, and resume the existing run state rather than creating an unrelated task.
- Publish only completed output. Expose final output after the run reaches completed according to your state rules; present a pending or failed state as such, not as a successful partial answer.
- Apply operational controls. Set environment, reviewer access, retention, and deletion behavior to match your application policy, and recheck the service’s current data controls.
The contract is useful when it lets a person reconstruct what happened and what remains to be done without confusing output with completion, missing evidence with a clean result, or a review pause with a new run.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
