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Different fields can still contradict one another. This alternative suggests “Approve the invoice because each currency code is individually valid”, whereas the supported approach is “Run a cross-field consistency validation before approval”. Semantic relationships between fields are outside basic syntax checks. Syntactically valid data may still fail arithmetic, cross-field, policy or source-evidence checks, which require separate deterministic validation and useful feedback.

“Discard currency codes and assume every amount is USD” is the wrong action for this case. The underlying source might use another currency. The supported choice is “Run a cross-field consistency validation before approval”. Semantic relationships between fields are outside basic syntax checks. Recompute totals and constraints from the source values, report precise violations, and retry only the affected output instead of accepting plausible JSON blindly.

Scale and unit coherence must be checked. The proposal “Convert totals without verifying which currency the line items use” conflicts with the required decision. “Run a cross-field consistency validation before approval” addresses the actual condition. Semantic relationships between fields are outside basic syntax checks. Syntactically valid data may still fail arithmetic, cross-field, policy or source-evidence checks, which require separate deterministic validation and useful feedback.

“Keep retrying the same invalid format with no new feedback” is the wrong action for this case. A bounded failure should be surfaced instead. The supported choice is “Stop at a bounded retry limit and surface the persistent failure”. Recovery loops need explicit failure handling instead of endless retries. Recompute totals and constraints from the source values, report precise violations, and retry only the affected output instead of accepting plausible JSON blindly.

Exhaustion is not successful validation. The proposal “Mark the record valid after retries are exhausted” conflicts with the required decision. “Stop at a bounded retry limit and surface the persistent failure” addresses the actual condition. Recovery loops need explicit failure handling instead of endless retries. Syntactically valid data may still fail arithmetic, cross-field, policy or source-evidence checks, which require separate deterministic validation and useful feedback.

The caller needs a durable error and affected item ID. This alternative suggests “Silently drop the malformed record from the batch”, whereas the supported approach is “Stop at a bounded retry limit and surface the persistent failure”. Recovery loops need explicit failure handling instead of endless retries. Syntactically valid data may still fail arithmetic, cross-field, policy or source-evidence checks, which require separate deterministic validation and useful feedback.

String type may violate the numeric contract. The proposal “Treat any numeric-looking text as valid currency amount data” conflicts with the required decision. “Validate numeric field types and apply the expected normalization rules” addresses the actual condition. Well-formed JSON can still violate declared field semantics. Syntactically valid data may still fail arithmetic, cross-field, policy or source-evidence checks, which require separate deterministic validation and useful feedback.

Normalization must preserve financial meaning. This alternative suggests “Strip units without checking the amount's original scale”, whereas the supported approach is “Validate numeric field types and apply the expected normalization rules”. Well-formed JSON can still violate declared field semantics. Syntactically valid data may still fail arithmetic, cross-field, policy or source-evidence checks, which require separate deterministic validation and useful feedback.

“Approve the record solely because the braces form valid JSON” is the wrong action for this case. JSON syntax can be valid while a money field still contains the wrong primitive type, so field-level type checks remain necessary. The supported choice is “Validate numeric field types and apply the expected normalization rules”. Well-formed JSON can still violate declared field semantics. Recompute totals and constraints from the source values, report precise violations, and retry only the affected output instead of accepting plausible JSON blindly.

A systemic false-positive class needs an explicit rule change. This alternative suggests “Tell every individual reviewer to be more careful”, whereas the supported approach is “Analyze dismissed finding patterns and refine the categorical review rule”. Feedback aggregation exposes prompt-wide problems better than one-off dismissals. Syntactically valid data may still fail arithmetic, cross-field, policy or source-evidence checks, which require separate deterministic validation and useful feedback.

“Remove all warnings, including proven high-risk findings” is the wrong action for this case. Calibration should target dismissed patterns specifically. The supported choice is “Analyze dismissed finding patterns and refine the categorical review rule”. Feedback aggregation exposes prompt-wide problems better than one-off dismissals. Recompute totals and constraints from the source values, report precise violations, and retry only the affected output instead of accepting plausible JSON blindly.

Model capacity alone does not define the desired exclusions. The proposal “Use a larger model without analyzing dismissed examples” conflicts with the required decision. “Analyze dismissed finding patterns and refine the categorical review rule” addresses the actual condition. Feedback aggregation exposes prompt-wide problems better than one-off dismissals. Syntactically valid data may still fail arithmetic, cross-field, policy or source-evidence checks, which require separate deterministic validation and useful feedback.

A factor-of-100 discrepancy can survive a naive sum. This alternative suggests “Check only subtotal addition while ignoring currency and scale changes”, whereas the supported approach is “Check currency, scale and arithmetic coherence for each record”. Schema compliance alone cannot catch unit mismatches. Syntactically valid data may still fail arithmetic, cross-field, policy or source-evidence checks, which require separate deterministic validation and useful feedback.

“Trust a numeric parser even when the source mixes cents and dollars” is the wrong action for this case. Units are necessary for interpretation. The supported choice is “Check currency, scale and arithmetic coherence for each record”. Schema compliance alone cannot catch unit mismatches. Recompute totals and constraints from the source values, report precise violations, and retry only the affected output instead of accepting plausible JSON blindly.

The tolerance must reflect actual financial rules. The proposal “Accept any total within the same order of magnitude” conflicts with the required decision. “Check currency, scale and arithmetic coherence for each record” addresses the actual condition. Schema compliance alone cannot catch unit mismatches. Syntactically valid data may still fail arithmetic, cross-field, policy or source-evidence checks, which require separate deterministic validation and useful feedback.

“Use an interactive synchronous call for every document in a latency-tolerant nightly job” is the wrong action for this case. It sacrifices available asynchronous throughput and cost advantages. The supported choice is “Use asynchronous message batches for latency-tolerant work”. Asynchronous Message Batches fit a deadline-tolerant nightly document workload and can lower processing cost, provided the job retains per-document result IDs. Control batch size, isolate failures, and confirm that aggregation has all required records and fields before declaring the job complete.

Batch completion is not an immediate SLA. The proposal “Depend on a batch job completing within a guaranteed few minutes” conflicts with the required decision. “Use asynchronous message batches for latency-tolerant work” addresses the actual condition. Asynchronous Message Batches fit a deadline-tolerant nightly document workload and can lower processing cost, provided the job retains per-document result IDs. Batch extraction is reliable when each record retains its identity and evidence while missing or malformed input is reported explicitly rather than silently replaced.

Nightly processing still needs per-document identifiers. This alternative suggests “Use untracked submissions so results cannot be mapped back to source files”, whereas the supported approach is “Use asynchronous message batches for latency-tolerant work”. Asynchronous Message Batches fit a deadline-tolerant nightly document workload and can lower processing cost, provided the job retains per-document result IDs. Batch extraction is reliable when each record retains its identity and evidence while missing or malformed input is reported explicitly rather than silently replaced.

The result may arrive after the developer needs a decision. The proposal “Use an overnight batch for the blocking merge gate” conflicts with the required decision. “Use the synchronous API for the blocking merge decision” addresses the actual condition. Batch completion is not appropriate when latency cannot be tolerated. Batch extraction is reliable when each record retains its identity and evidence while missing or malformed input is reported explicitly rather than silently replaced.

The required security check has not run. This alternative suggests “Mark the pull request approved before the batch finishes”, whereas the supported approach is “Use the synchronous API for the blocking merge decision”. Batch completion is not appropriate when latency cannot be tolerated. Batch extraction is reliable when each record retains its identity and evidence while missing or malformed input is reported explicitly rather than silently replaced.

“Infer approval from a prior unrelated scan result” is the wrong action for this case. A current merge decision needs current evidence. The supported choice is “Use the synchronous API for the blocking merge decision”. Batch completion is not appropriate when latency cannot be tolerated. Control batch size, isolate failures, and confirm that aggregation has all required records and fields before declaring the job complete.

Individual outcomes become difficult to reconcile. This alternative suggests “Send all documents in one request with no custom IDs”, whereas the supported approach is “Submit independent requests in batches with stable correlation identifiers”. Batched processing scales independent work and preserves result matching. Batch extraction is reliable when each record retains its identity and evidence while missing or malformed input is reported explicitly rather than silently replaced.

“Process documents as one enormous prompt despite context limits” is the wrong action for this case. The workload is independent and better partitioned. The supported choice is “Submit independent requests in batches with stable correlation identifiers”. Batched processing scales independent work and preserves result matching. Control batch size, isolate failures, and confirm that aggregation has all required records and fields before declaring the job complete.

Batch results should be joined by stable identifiers. The proposal “Assume results will return in submission order” conflicts with the required decision. “Submit independent requests in batches with stable correlation identifiers” addresses the actual condition. Batched processing scales independent work and preserves result matching. Batch extraction is reliable when each record retains its identity and evidence while missing or malformed input is reported explicitly rather than silently replaced.

Already successful documents would be needlessly reprocessed. This alternative suggests “Resubmit the entire batch after two context-limit failures”, whereas the supported approach is “Resubmit only the failed document requests after reducing their input size”. Successful outputs need not be recomputed. Batch extraction is reliable when each record retains its identity and evidence while missing or malformed input is reported explicitly rather than silently replaced.

“Retry the same oversized inputs without changing them” is the wrong action for this case. The same oversized document still exceeds the model context budget; a retry requires shortening or partitioning that specific input. The supported choice is “Resubmit only the failed document requests after reducing their input size”. Successful outputs need not be recomputed. Control batch size, isolate failures, and confirm that aggregation has all required records and fields before declaring the job complete.

Per-request outcomes must remain accurate. The proposal “Mark the failed documents successful because most others completed” conflicts with the required decision. “Resubmit only the failed document requests after reducing their input size” addresses the actual condition. Successful outputs need not be recomputed. Batch extraction is reliable when each record retains its identity and evidence while missing or malformed input is reported explicitly rather than silently replaced.

“Assume a client tool can execute mid-batch through ordinary message text” is the wrong action for this case. The batch API does not perform an interactive tool round trip. The supported choice is “Use an interactive API loop because batch requests do not perform mid-request client tool round trips”. Dependent multi-turn tool execution needs a running controller. Control batch size, isolate failures, and confirm that aggregation has all required records and fields before declaring the job complete.

The result is needed during agent reasoning. The proposal “Run the external action after declaring the batched task complete” conflicts with the required decision. “Use an interactive API loop because batch requests do not perform mid-request client tool round trips” addresses the actual condition. Dependent multi-turn tool execution needs a running controller. Batch extraction is reliable when each record retains its identity and evidence while missing or malformed input is reported explicitly rather than silently replaced.

It would conceal real application execution. This alternative suggests “Use a single batch job with fabricated tool_result messages”, whereas the supported approach is “Use an interactive API loop because batch requests do not perform mid-request client tool round trips”. Dependent multi-turn tool execution needs a running controller. Batch extraction is reliable when each record retains its identity and evidence while missing or malformed input is reported explicitly rather than silently replaced.

The batch service provides no such guarantee. The proposal “Promise a fixed short completion time based on recent averages” conflicts with the required decision. “Allow a completion buffer and an operational fallback if the batch misses the deadline” addresses the actual condition. Non-guaranteed batch latency must be accounted for in scheduling. Batch extraction is reliable when each record retains its identity and evidence while missing or malformed input is reported explicitly rather than silently replaced.

Any delay would miss the business cutoff. This alternative suggests “Schedule the batch exactly at the deadline with no recovery option”, whereas the supported approach is “Allow a completion buffer and an operational fallback if the batch misses the deadline”. Non-guaranteed batch latency must be accounted for in scheduling. Batch extraction is reliable when each record retains its identity and evidence while missing or malformed input is reported explicitly rather than silently replaced.

“Discard late valid results instead of planning a controlled fallback” is the wrong action for this case. The workflow should account for late completion. The supported choice is “Allow a completion buffer and an operational fallback if the batch misses the deadline”. Non-guaranteed batch latency must be accounted for in scheduling. Control batch size, isolate failures, and confirm that aggregation has all required records and fields before declaring the job complete.