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Partitioning jobs by size helps throughput but still cannot supply application-run tool results in the middle of one batch request. This workflow has multi-turn dependencies requiring a live caller. Use interactive orchestration instead of expecting asynchronous batch processing to pause for local tool execution.

Asynchronous batches need not preserve original input order. This alternative does not establish how to keep live tool exchanges in an interactive execution path in the stated workflow.

Asynchronous batches are unsuitable when the customer is waiting on an immediate response. Although useful elsewhere, it is not the direct mechanism to keep live tool exchanges in an interactive execution path.

Redacted logging protects customer data, but it cannot make an asynchronous batch meet an SLA requiring every answer inside five minutes. Choose a synchronous or latency-guaranteed workflow for that service deadline; separate data-minimization controls still apply.

External batch output may not remain retrievable indefinitely. This capability does not directly respect service-level latency constraints. Retrieving finished results within retention addresses data availability, but it does not guarantee a five-minute latency SLA for an asynchronous job. Select a processing mode compatible with the deadline.

Asynchronous generation does not eliminate output errors. The proposed option cannot provide the specific “The asynchronous completion window cannot guarantee the required response latency” behavior that the case requires. An asynchronous batch with an uncertain completion window is incompatible with a five-minute deadline.

Asynchronous batches need not preserve original input order. The decisive requirement here is to avoid accidental duplicate batch workloads. Mapping results by custom_id avoids mismatched outputs, yet it does not prevent an operator from creating two whole batches of the same documents. Keep a submission-level manifest and deduplication key.

Batch processing suits large noninteractive work with relaxed deadlines and cost sensitivity. This alternative does not establish how to avoid accidental duplicate batch workloads in the stated workflow.

Asynchronous batches are unsuitable when the customer is waiting on an immediate response. It would leave the stated need to avoid accidental duplicate batch workloads unresolved.

Retrieving and storing batch outputs before expiration preserves their availability, but does not establish that ten thousand classifications satisfy their schema or business rules. Validate each returned result before loading it, then separately persist the accepted data.

Lower cost is irrelevant if the processing pattern violates the service's deadline. The scenario instead requires a way to enforce per-item correctness in bulk ingestion.

Partial success should not trigger repetition of the entire job. This is not a substitute for “Validate every returned item against its schema and business rules before loading”; that control is necessary in the stated circumstances. Validate returned item schemas, allowed values and business invariants before loading ten thousand outputs.

Persisting outputs within a retention window matters after a batch finishes, but a growing backlog should not become one untracked monolith. Partition work into bounded batches with stable IDs, checkpoints and provider-limit awareness so partial failures can be recovered independently.

Lower cost is irrelevant if the processing pattern violates the service's deadline. The decisive requirement here is to scale batch workloads with bounded trackable partitions.

Polling terminal job status is necessary, but one huge unpartitioned batch still causes poor traceability and coarse retry behavior. Split by manageable input sizes and track each work unit, then monitor the resulting batches for completion or failure.

Synchronous orchestration is appropriate when each item needs live human approval or a client-executed callback before continuing. This collection has independent asynchronous document processing and no such dependency. Message Batches can run multi-turn content and server tools, so its suitability depends on required interaction and latency rather than a blanket tool-cycle restriction.

Batch throughput does not remove the need for exactly-once or idempotent application behavior. Here, a large document collection can be processed independently without immediate responses. Use an asynchronous batch API and correlate results with custom_id values. Batch execution is cost-effective for independent jobs while identifiers preserve result association.

Scoped retries avoid paying to repeat valid work and preserve traceability. The proposed option cannot provide the specific “Use an asynchronous batch API and correlate results with custom_id values” behavior that the case requires. Batch execution is cost-effective for independent jobs while identifiers preserve result association.

A batch can process independent multi-turn conversations and supported server-executed tools, but it cannot stop midrequest to wait for an immediate customer approval callback and a host-executed client-tool result. This particular side effect needs a live application-controlled loop that validates the customer's decision before continuing.

A completed batch’s failure record does not show that an approval was asked or granted before an attempted side effect. Approvals must precede the action and be tied to the exact pending request. Reprocessing only failed items is useful recovery but cannot substitute for the required live decision boundary.

Splitting the workload into small batches changes throughput, not the control flow that obtains customer approval before each side effect. Even a one-item asynchronous batch lacks the immediate interactive callback contract the scenario requires. Use a synchronous host-driven loop with authorization and recorded decisions.

Scoped retries avoid paying to repeat valid work and preserve traceability. The direct remedy is “Deduplicate using stable record identity before processing”, which the alternative does not itself accomplish. Batch throughput does not remove the need for exactly-once or idempotent application behavior.

Balanced batches control failure blast radius and improve predictable throughput. Here, a batch contains duplicate records that would trigger duplicate writes downstream. Deduplicate using stable record identity before processing. Batch throughput does not remove the need for exactly-once or idempotent application behavior.

Asynchronous completion may not match submission order, so stable correlation is essential. The issue here is a batch contains duplicate records that would trigger duplicate writes downstream, so the relevant control is to deduplicate using stable record identity before processing. Batch throughput does not remove the need for exactly-once or idempotent application behavior.

Balanced batches control failure blast radius and improve predictable throughput. The proposed option cannot provide the specific “Reprocess only failed items with persistent identifiers and an error ledger” behavior that the case requires. Scoped retries avoid paying to repeat valid work and preserve traceability.

this choice instead describes join results by custom_id rather than trusting completion order. The issue here is a large batch returns a mixture of successes and failures, so the relevant control is to reprocess only failed items with persistent identifiers and an error ledger. Scoped retries avoid paying to repeat valid work and preserve traceability.

Batch execution is cost-effective for independent jobs while identifiers preserve result association. This is not a substitute for “Reprocess only failed items with persistent identifiers and an error ledger”; that control is necessary in the stated circumstances. Scoped retries avoid paying to repeat valid work and preserve traceability.

Matching Message Batch results by custom_id is essential for associating asynchronous results with submitted inputs, but it does not reduce the processing delay caused by one unusually large document. The scenario describes uneven document sizes, so partition work by estimated input complexity or size; keep identifier-based correlation as a separate requirement.

Batch execution is cost-effective for independent jobs while identifiers preserve result association. The proposed option cannot provide the specific “Partition work by input size or complexity rather than fixed document count alone” behavior that the case requires. Balanced batches control failure blast radius and improve predictable throughput.

A synchronous controller is appropriate for immediate user approvals or caller-run tool callbacks, not merely because documents differ in processing cost. These batch inputs can be processed asynchronously. Partitioning by expected size or complexity handles uneven throughput without requiring a user-facing interaction on each request.

Batch results may be delivered in an order different from the submitted input sequence. Associating them solely by position is unsafe even when all identifiers are unique.

A custom_id is not a decorative label: result consumers use it to match the output to the input customer document. Reusing one ID for different records makes that mapping ambiguous. Require uniqueness within the batch before submission and preserve the source relationship.

Deleting the mapping removes the evidence necessary to join results back to source documents or investigate partial failure. Preserve identifiers with controlled access and retention.