Output failure
Schema Violation
Outputs that don't match a required format, contract, or structure — malformed JSON, bad fields, invalid tool arguments.
What failed
A schema violation occurs when an AI system produces output that does not match the required format, contract, or structure. This can include malformed JSON, missing required fields, invalid enum values, wrong data types, unsupported tool arguments, or responses that cannot be parsed by downstream systems.
Architecture context
Structured extraction, JSON generation, API calling, function calling, database updates, workflow automation, form filling, and systems that pass model outputs into downstream code.
Impact
Schema violations can break workflows even when the model natural-language reasoning is mostly correct. In agent systems, schema errors can cause failed tool calls, retries, data corruption, incorrect automation, or cascading failures across services.
Symptoms
- Malformed JSON or XML.
- Missing required fields.
- Invalid field names or data types.
- Tool arguments fail validation.
- Output includes commentary inside structured payloads.
- Downstream parser or API rejects the response.
Detection signals
- Parse failure rates.
- Validation errors.
- Tool-call argument errors.
- Retry frequency after format failures.
- Mismatch between expected and actual output schema.
- Error spikes after prompt or model changes.
Mitigations
- Use strict schema validation.
- Apply constrained decoding or structured-output modes where available.
- Keep schemas simple and explicit.
- Add automatic repair only when safe.
- Fail closed for high-risk actions.
- Regression-test structured outputs after changes.