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Averaging incompatible contract totals produces a third figure that neither source page actually states. The documents may represent different amendments, effective dates or billing bases, so arithmetic compromise is not source resolution. Preserve the exact amounts, page citations and apparent conflict, then identify which clause or signed amendment controls. If the governing total cannot be established, flag the contract field as disputed instead of inventing a blended amount.

Omitting the contradiction and marking extraction complete hides a material source uncertainty. Downstream legal or financial decisions might rely on whichever amount the model happened to choose first, even though another authoritative-looking page disagrees. The validator must retain both source spans and require explicit precedence or expert clarification. A clean JSON record is not evidence that the conflicting contract terms were reconciled correctly.

The larger amount is not automatically the controlling contract value. It might be a previous proposal, an uncapped estimate or a superseded schedule, while a smaller figure could be the signed current total. Risk aversion is not a rule for selecting between documentary facts. Compare effective dates, signatures, cross-references and amendment terms before adopting one. If the pages remain inconsistent, preserve the uncertainty and request an authorized resolution.

The JSON number type says the field is parseable as a numeric value, not that the value has exact cent precision. Binary floating-point can introduce representation artifacts for decimal fractions, and a contract that accepts any number will not detect the discrepancy between the receipt’s USD 0.30 and the serialized result. The consumer must enforce a monetary representation and rounding contract independent of the output schema, then compare the amount with the observed source. Type validation alone cannot certify an exact financial amount.

Asking the language model to regenerate the same number does not remove the numeric representation error in the host’s arithmetic. A later response might look shorter while still being evaluated through the same binary-float code path, so text sampling is not a reliable repair for an exact-cents invariant. Inspect how the input decimals are parsed and combined, switch to decimal or minor-unit arithmetic, and test representative fractions and rounding boundaries. A retry without changing the computation can hide the problem rather than solve it.

A downstream payment processor may reject excessive fractional digits, round differently or silently convert them into a distinct stored representation. Accepting that uncertainty is inappropriate when the receipt explicitly records USD 0.30. The host should validate amounts in a currency-appropriate numeric domain before submission, verify the rounded minor-unit result against source evidence, and report any discrepancy. Treating the processor as a guessed repair mechanism gives up control over a material financial invariant and weakens auditability.

Renaming an HTML response does not convert it into a PDF. A parser or citation system may later treat the placeholder as source evidence and invent page locations. The host should inspect actual response bytes, document type and access state before saving the item as verified. When a login page appears, record the retrieval limitation and require authorized access rather than silently changing the file extension.

HTTP 200 cannot establish document identity when a service redirects to an authentication page or an error message. Content-specific validation must inspect the actual payload, not just the status code. Treating any successful transport as the target source would contaminate downstream facts and citations. The correct behavior is to surface the mismatch and obtain the PDF only through a supported authorized route.

An unrelated cached PDF does not answer the requested source question, even if it contains superficially similar text. Substitution would hide provenance and may introduce a different edition or vendor policy. Keep the inaccessible original source clearly marked and use a mirror only with a transparent attribution caveat. Source verification requires exact artifact identity and content, not a plausible replacement.

A network timeout means a response is missing or delayed; an explicit deleted-item error is an observed application-level result. Treating it as a transient transport problem would cause the same invalid ID to be retried indefinitely. The next step is to stop that request path, check whether a legitimately related current ID exists and report the deletion if not. Error classification should control retry policy.

Returning successful empty data would erase the provider's statement that the requested object no longer exists. Downstream code could mistake the empty object for a real record with blank fields and make incorrect updates or user statements. Preserve the terminal not-found status with the actual attempted identifier, and use a separate authorized discovery tool if replacement lookup is appropriate. An empty success cannot repair a missing resource.

Increasing output tokens does not change what the provider knows about a deleted item. The failure is not truncated text generation but a nonexistent target resource, so another call with the same ID is unlikely to succeed. Supply a specific not-found classification, stop condition and supported search path in the tool feedback. Reserve retries for conditions that could change through a permitted argument or external state update.

Replacing error_code with an empty string makes the record appear coherent without establishing which reported state was true. A service that succeeded might retain nonfatal diagnostic metadata, or a failure might have been mislabeled as success; only its documented status contract resolves the combination. Do not silently erase conflicting evidence. Validate the provider's semantics or request a corrected result before accepting the record into an approved classification dataset.

Storing contradictory states indefinitely as valid leaves consumers unable to know whether to proceed, retry or stop. A success flag could trigger downstream publication while the error field indicates a failed prerequisite. Preserve the raw record for audit, but put the processing state into invalid or needs-review until the provider contract resolves the conflict. Data retention is different from declaring semantic acceptance.

Deleting error_code and retaining success assumes the optimistic field is authoritative without evidence. It can hide failed operations and make later reconciliation impossible. Check the actual provider status, response contract and any correlated operation ID before resolving inconsistent fields. If the conflict cannot be explained, do not convert it to success; return a validation failure with source context.

Regenerating addresses, amounts and dates is unrelated to a surname-field violation and introduces new opportunities for regression. Those unrelated fields were not reported incorrect, and a broad regeneration could overwrite source-verified values. Request a focused correction with the specific name span and validator feedback, then confirm that all other fields remain unchanged. Editing scope should track the actual failing condition, not the size of the whole document.

Requiring exactly three name tokens mistakes a cultural or formatting pattern for the user's verified field schema. People may have one name, compound family names, suffixes or multiple given names. A token-count shortcut would still misclassify many valid records and can create artificial surnames. Use labeled source data and field semantics, report ambiguity, and seek clarification when the document does not uniquely establish the surname.

A dictionary entry does not supply the person's actual surname and would fabricate a personal data field. Even a common family name is not evidence that it belongs to the source record. The extractor should retain an unknown or validation error when the surname is not supported, or correct the mapping using the original labeled text if it is clear. Automatic completion from plausible names is not a factual repair.

Adding a blank record to reach the expected row count produces a superficially complete file but does not restore the omitted final source row. The missing row may carry a customer identifier or monetary amount, and a placeholder cannot prove it was processed. The validator must compare the full source row-ID set with the extracted record IDs and report actual missing or duplicate entries. Fix the extraction for the lost row, then validate values and ordering; arithmetic count equality alone is not sufficient evidence of faithful conversion.

Sampling can help assess extraction quality but will not detect a missing final row if it lies outside the sample. The failure described is a completeness defect, not merely an inaccurate field in a sampled record. Compare expected source-row identifiers and counts with all output records, then investigate missing or duplicate IDs. A valid-looking sample cannot substitute for whole-dataset coverage accounting.

The agent’s sentence that it read everything is a self-report, not an inventory of the source and output records. Here the actual structured output lacks the last spreadsheet row despite that possible claim. A separate check should reconcile source row IDs, counts and any expected order, and identify missing or duplicated records before acceptance. Otherwise a fluent completion summary can hide data loss that affects the final customer or invoice. Verify the transformed artifact, not merely the model’s statement about the workflow.

Skipping comparison with previously verified fields allows a targeted address fix to silently corrupt an invoice ID that was already correct. The repaired record needs invariant checks preserving unchanged identifiers, totals and required metadata. The original verified values form part of the expected output contract. A successful local edit is not sufficient if the full document no longer describes the same invoice.

Removing the invoice ID from the schema hides the regression rather than preserving the record's identity. The consumer may need that identifier to reconcile the repaired invoice with its original transaction. A valid address cannot compensate for losing a required verified key. Keep the identifier in the contract and reject any patch that changes it without authorized source evidence. Schema removal is not a repair for a generator that touches unrelated fields.

Replacing the entire invoice for a one-field address correction gives the model or transformation many opportunities to alter amounts, dates, IDs or line items that were already correct. That is broader than the observed failure and complicates the audit trail. Use a limited field patch with source evidence and regression checks on the full record. A full rewrite might be appropriate in a different workflow, but it is an unnecessary risk here.

A well-formed citation string can point to an inaccessible or unseen document. The stated failure is unsupported factual claims, so structural citation checks cannot substitute for source readback. The audit needs to trace each material assertion to evidence actually retrieved from the PDF or clearly label its limitation. A valid URL syntax alone does not establish page content or the truth of a statement.

More restrictive JSON types can stop extra fields or malformed output, but a well-formed assertion about an unseen PDF can still be false. The model may produce a perfectly typed citation object without ever retrieving the document or checking the claimed paragraph. To establish source support, the host must fetch the actual PDF or expose that it was inaccessible, inspect the relevant content and compare the proposed fact with it. Schema strictness is not a substitute for document readback or provenance.

Removing uncertainty wording makes the final answer more assertive while leaving its evidence gap unchanged. The PDF was not read, so the system cannot know whether its contents support the reported statement. A validator should check access and cited source spans before approving document-specific claims; if access remains blocked, identify that limit or remove the unsupported claim. Confidence styling does not turn an unobserved source into evidence, and can increase harm by encouraging reliance on an invented reference.

Dropping leading zeros changes an account code's identity: 000123 may be a meaningful identifier rather than the integer 123. The schema error could indicate that the field was modeled incorrectly as a number. Inspect the business definition and original source before altering the value. If zeros are significant, use a string schema and preserve the exact code; do not 'fix' valid source data to satisfy an inappropriate type constraint.

Casting every field to integer may silently destroy account identifiers, postal codes or values containing leading zeros. It also discards units and fails on genuine text fields. The repair must be selective and based on field semantics, not a blanket conversion rule. A numeric-looking identifier can remain a string even if it contains only digits. Validate the consumer's true contract before coercion.

Repeatedly submitting the same string to the same integer validator is unlikely to change a deterministic schema rejection. It wastes retries while leaving the potential contract mismatch unresolved. Inspect whether zeros are meaningful, correct the schema or value conversion as justified, and return a typed error when no safe repair is available. A retry becomes useful only after the input or relevant contract changes.

Adding every synonym to the allowed enum whenever a model produces it would make the taxonomy drift unpredictably. Downstream reporting relies on stable, approved labels, especially when complaints arrive in different languages. The validator should reject an out-of-set label, provide the exact allowed values and request reconsideration against the original complaint evidence. New taxonomy values require deliberate product governance, not automatic expansion triggered by one generated phrase.

Translation can be a legitimate preprocessing step for multilingual content, but an uncontrolled nearest-label fallback hides taxonomy errors and may distort the original complaint. The validator has already identified an out-of-vocabulary label. Return the allowed enum and relevant evidence so a bounded retry can select an authorized label or disclose ambiguity. Do not silently force an unsupported category into the result.

An out-of-taxonomy label may look descriptive to a human while remaining invalid for the software that consumes the classification. Treating any new string as acceptable prevents reliable aggregation and can route a complaint to a nonexistent workflow. The host should preserve the specific invalid value, communicate the allowed labels and ask for a supported correction or explicit unknown. Validator failure must not be transformed into a successful classification merely because the prose seems plausible.