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An AI run reaching a terminal or paused state does not, by itself, prove that you received its final answer or that the external change you requested occurred. Track three checkpoints separately: execution state, result availability, and confirmation of the intended outcome.
What “finished” can—and cannot—tell you
AI systems expose lifecycle signals, but their names and meanings depend on the runtime. A completed execution may tell you that the runtime reached a particular state; it does not automatically establish that a caller received a usable result or that another system reflects the requested change.
Use three distinct checks:
- Execution: Did the run finish, fail, pause for approval, or get cancelled?
- Result: Is the final output present, persisted, and retrievable by the caller?
- Outcome: Does the system responsible for the external effect confirm the requested postcondition?
The first two checks concern the agent runtime and its output. The third concerns the application or service where the effect should appear. Treating them as separate checkpoints is an engineering recommendation, not a universal lifecycle rule shared by every platform.
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Why a run can stop without delivering a final answer
It paused for a decision
An interruption can mean the run needs approval or another decision before it can continue. OpenAI’s Agents SDK documentation says an interrupted run returns state rather than a final answer; finalOutput or final_output may be empty because the run has not finished, while interruptions reports pending tool calls that need a decision. Inspect that state and use the SDK’s documented resume path rather than treating the pause as success. OpenAI Agents SDK: Human in the loop OpenAI Agents SDK: Running agents
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A stream ended, but cleanup or the turn did not complete
In a streaming SDK, visible text or an apparent endpoint is not necessarily the completion boundary. OpenAI’s JavaScript guide explains that a cancelled stream’s completion signal resolves after cleanup, while final-output fields can remain unset if the turn did not finish. Its Python guide says to consume stream_events() fully before inspecting settled properties and to allow cleanup to complete after cancellation. Check the documentation for the specific SDK you use; do not assume stream termination and turn completion are interchangeable. OpenAI Agents SDK: Streaming OpenAI Agents SDK: Running agents
An asynchronous job has its own status vocabulary
Background-job states are defined by each platform. In the Gemini API, a background interaction runs asynchronously on the server, and completed means its output is available. That label describes Gemini’s interaction model; it is not a standard shared by all agent runtimes. Gemini API: Background execution
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The versioned NVIDIA AIQ Blueprint 1.2.1 architecture guide lists SUBMITTED, RUNNING, SUCCESS, FAILURE, and INTERRUPTED. It says a final report is available on success, and describes a background reaper that marks stale running jobs as failure after a timeout. These are Blueprint-specific lifecycle details, not universal meanings for those labels. NVIDIA AIQ Blueprint 1.2.1 architecture
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- Read the runtime’s authoritative state. Use its status or run-result surface to distinguish a terminal state from interruption, failure, or cancellation. Interpret labels using documentation for the API or SDK version in use.
- Retrieve the final result independently of the initial acknowledgment. Keep a stable task or run identifier and provide a way to fetch its state and output later. For streams, wait for the SDK’s documented completion and cleanup boundary; for interruptions, inspect pending decisions and resumable state.
- Verify the requested effect at its source. Read back the relevant record, resource, or transaction from the system that owns it, or use another authoritative signal appropriate to the application. A final answer that says an action succeeded is not independent confirmation that the postcondition holds.
- Only then decide whether to resume, retry, or report completion. If the caller did not receive an answer, first inspect the original run’s durable state and check the external system. Blindly replaying an effectful operation can create duplicate or conflicting effects.
Persisting a task identifier and exposing status, result retrieval, and outcome verification as separate operations makes each checkpoint observable. The verification mechanism depends on the application’s external system; the cited runtime documentation does not establish a universal postcondition check.
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What to compare when choosing or integrating a runtime
Evaluate the lifecycle features that matter to your workflow, while keeping each platform’s own state semantics attached to its documentation:
- Does it distinguish success, failure, cancellation, and interruption?
- Can a caller retrieve the final result after a disconnect or delayed acknowledgment?
- Does it expose resumable state for interruptions, and could resuming repeat side effects?
- How will your application independently verify the requested postcondition?
- Are the lifecycle details documented for the exact SDK, API, or product version you deploy?
Some products also document validation processes that are separate from execution. MongoDB Atlas Agent Engine describes a human-in-the-loop lifecycle in which the effect of a decision depends on the agent code. Microsoft Discovery describes an independent process for validating a result and reviewing execution history. These are product-specific examples, not requirements that apply to every agent system. MongoDB Atlas Agent Engine: Human in the loop Microsoft Discovery: Approval requirements
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