A terminal or paused AI execution does not, by itself, prove that its answer reached the caller—or that the requested change happened. Check three things separately: whether execution ended, whether its result is available, and whether the intended outcome is confirmed.
What “finished” does—and doesn’t—tell you
AI workflows can expose lifecycle states, streamed events, and final outputs, but those signals answer different questions. A run may stop without producing a final answer; a result may exist without reaching the caller; and an answer describing an action does not independently verify that an external system changed as requested.
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There is no universal state model across agent runtimes. For example, the OpenAI Agents SDK documents interrupted runs that return state rather than a final answer, while Gemini API uses completed for a background interaction whose output is available. NVIDIA AIQ Blueprint 1.2.1 documents its own job states. Interpret labels according to the specific product and version, not as shared standards.
Check the three checkpoints separately
1. Execution: what happened to the run?
Read the runtime’s documented status or result surface to determine whether the run finished, failed, was cancelled, or paused for a decision. An interruption is not the same as successful completion. In the OpenAI Agents SDK, an interrupted run can include pending tool calls that need a decision, and its finalOutput or final_output may be empty because the run has not finished. The SDK documentation describes interruptions as the place to inspect those pending calls. See the OpenAI Agents SDK guide to running agents and its Python running-agents guide.
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Follow the runtime’s documented resume or decision path when a run is interrupted. Do not treat a missing final answer as proof that the run succeeded, or assume the work should simply be launched again.
2. Result: can the caller retrieve the output?
Check whether the final output is present, persisted, and retrievable—not merely whether the process stopped or emitted text. In the Gemini API, background interactions run asynchronously on the server, and the documentation says output is available when the interaction is completed. That meaning belongs to this API; it should not be generalized to another runtime. See the Gemini API background interaction documentation.
For streams, an apparent endpoint or visible text may not mark the point when the SDK has finished cleanup and settled its final fields. OpenAI’s JavaScript guide says that after cancellation, the completion signal resolves after cleanup, while final-output fields can remain unset if the turn did not finish. Its Python guide likewise says to finish consuming stream_events() before inspecting settled properties, and to allow cleanup to complete after cancellation. Consult the relevant JavaScript streaming guide or Python running-agents guide.
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For an asynchronous job, use the runtime’s documented retrieval path after checking its status. NVIDIA AIQ Blueprint 1.2.1 lists SUBMITTED, RUNNING, SUCCESS, FAILURE, and INTERRUPTED; it says a final report is available for successful jobs. Its architecture documentation also says a background reaper marks stale running jobs as failure after a timeout. Those are Blueprint-specific semantics, not universal lifecycle rules. See the AIQ Blueprint 1.2.1 async-jobs documentation.
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3. Outcome: did the requested change actually occur?
When a task is supposed to change an external system, verify the relevant postcondition in that system or through another authoritative signal. For example, if an agent is meant to update a record, check the record’s resulting state; if it is meant to trigger a workflow, check the workflow’s own status. Treat the agent’s message and the runtime’s successful status as evidence about the run, not independent confirmation of the external effect.
This is an engineering safeguard, not a guarantee made across the cited platforms. MongoDB Atlas Agent Engine’s human-in-the-loop documentation notes that the outcome of a decision depends on the agent code, illustrating why a human decision and the agent’s remaining work are separate lifecycle concerns. Microsoft Discovery documents an architecture with an independent process that validates a result and reviews execution history; that is a product-specific example, not a universal requirement. See the MongoDB Atlas Agent Engine human-in-the-loop documentation and Microsoft Discovery architecture documentation.
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Design workflows so a missing acknowledgment is recoverable
A caller can lose its connection or miss an acknowledgment even when the original run has durable state. A practical design is to give each task a stable identifier and make three operations distinct: read its status, retrieve its result, and verify its intended outcome. That separation lets a caller recover without confusing a delivery problem with an execution failure.
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- Expose lifecycle status. Report the runtime’s documented state, including interruptions and failures where available, rather than reducing every stopped run to “done.”
- Make result retrieval explicit. Allow the caller to fetch the final output from durable state, and follow the SDK’s completion or cleanup boundary for streamed results.
- Verify the postcondition separately. Read back the relevant state from the system that owns the external effect, or use its authoritative confirmation signal.
- Check before retrying effectful work. If an acknowledgment is missing, inspect the original run and external state before replaying an operation that could create a duplicate or conflicting effect.
These are general design recommendations inferred from the distinction between runtime status, output availability, and external state. The exact APIs, resumability behavior, and replay risks depend on the runtime and the application.
Questions to ask when evaluating a runtime
- Does its documentation distinguish success, failure, cancellation, and interruption?
- Can a caller retrieve a result after a disconnect or delayed acknowledgment?
- For an interruption, is resumable state available, and could resuming or replaying repeat side effects?
- What system or signal can independently confirm the application’s requested postcondition?
- Are the lifecycle labels and retrieval semantics documented for the specific API or SDK version in use?
These checks help expose gaps in an application’s recovery path. A runtime’s status vocabulary alone cannot answer whether the application’s external effect occurred.
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