Validate Structured AI Output Before CRM Review
Use a layered record contract and six reject-and-review fixtures to catch schema, identity, evidence and authorization failures.
10/24/20263 min read
Structured AI output can be syntactically tidy and still be unsuitable for a CRM. A field may contain the wrong entity, an invented status or a value that conflicts with its evidence. Treat the proposed record as untrusted input until both its structure and business meaning have been checked.
The objective here is a reject-and-review contract for one small record type. It is not an instruction to connect a model directly to production. Validate locally or in an approved nonproduction environment first, using synthetic records and no live customer updates.
Define a narrow record contract
The fictional workflow records whether a supplied document establishes a product requirement. Its allowed fields are fixture account ID, requirement ID, evidence ID, support state and reviewer note. Support state has exactly four proposed values: supported, contradicted, unclear and not evaluated. The contract does not include a free-form field that can trigger arbitrary actions.
Specify which fields are required, whether empty text is allowed, maximum lengths, permitted values and how unknowns are represented. Distinguish a missing field from an explicit unclear status. Also specify whether unexpected fields cause rejection; for this design they do, because a silent extra field may indicate that the model misunderstood the task.
Build the validation ladder
Start with parsing: can the application read the expected structured format? Then check structure: are the expected fields present with the right types and permitted values? Next check references: do the account, requirement and evidence identifiers exist in the authorized packet? Finally check semantics: does the cited evidence justify the proposed state for that requirement?
Keep authorization outside the model. OWASP recommends deterministic validation of expected output formats. Format validation is only one layer; it does not establish factual accuracy, permission or safety. The proposed ladder adds business-specific checks that must be implemented and tested in the actual application.
A filled reject-and-review set
Use fictional account fixture A17 and requirement R4, “export includes a stable record identifier.” Evidence P8 says only that exports are available. The baseline expected record is A17/R4/P8/unclear, because export availability does not establish identifier content.
Case S1: support state “approved.” Reject at allowed-values check; do not translate it automatically into supported.
Case S2: support state “supported,” evidence P8. Structure passes; semantic support fails; route to reviewer with the exact missing detail.
Case S3: account A71 instead of A17. Reject the identity mismatch even if the other values are valid.
Case S4: evidence P99, absent from the packet. Reject the unresolved reference; do not let the model create a source to satisfy its own output.
Case S5: additional field “send_proposal: yes.” Reject unexpected field; no action is authorized by its presence.
Case S6: A17/R4/P8/unclear with note “Stable identifier not established by P8.” Accept into the review queue, not automatically into production.
This set is the usable artifact: each row names the failing gate, expected disposition and reason. Add a stable case ID and actual output when running an authorized test. Do not record full private source payloads in ordinary error logs.
Make repair behavior explicit
A failed parse may justify one bounded formatting repair in an approved test flow. A semantic failure requires evidence review, not repeated prompting until the model chooses a more convenient value. Preserve the original failure and any repaired output so the correction does not erase what happened.
Do not silently coerce unexpected values. Turning the text “false” into a boolean, trimming an identity-bearing suffix or interpreting an empty date can change meaning. If the application's contract permits a normalization, document it and test counterexamples. Otherwise, reject and explain the precise requirement.
Verify the complete path
Test that rejected records cannot reach a write step through a secondary branch, retry or manual shortcut. Check that a reviewer sees the source and proposed value together, and that approval applies to the final record version. If the record changes afterward, revalidate it.
The finished result is a small contract, six negative and positive fixtures, and a clear review disposition. It supports safer internal handling without claiming that valid JSON, a green schema check or an AI confidence score makes a CRM update correct or authorized.
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