Structured Output Reliability in Enterprise LLM Workflow Systems: A Study of Schema Validation, Recovery Policies, and Human Review

This preprint studies structured output reliability in enterprise LLM workflow systems. The paper evaluates six output-control conditions: free-form output, JSON-only prompting, schema validation, validation retry, validation repair, and a full reliability configuration with human-review routing. Using a 50-task synthetic software ticket-triage benchmark and OpenAI gpt-4o-mini, the study measures JSON validity, schema validity, category accuracy, customer-impact F1, automation acceptance, human-review burden, false confidence, and error types. The results show that structured JSON/schema conditions reached full JSON and schema validity, but semantic workflow failures remained, especially false confidence, priority mismatch, customer-impact mismatch, and human-review mismatch. The paper argues that valid JSON is necessary but not sufficient for reliable enterprise LLM automation; systems also need evidence validation, confidence calibration, recovery policies, and human-review routing. The associated implementation, benchmark data, context documents, and evaluation scripts are available at: https://github.com/chebrma99/ContextBench