Structured Output
The capability of AI models to generate responses in a specific, machine-parseable format—JSON, XML, typed objects—rather than free-form text. Structured output is essential for AI agents because their outputs often feed directly into other systems: a sales agent must output a JSON object that the CRM API accepts, a finance agent must produce structured transaction categories, and a coding agent must generate valid code in the correct language. Most major LLM providers now support constrained output schemas that guarantee valid structured responses.
Written by Max Zeshut
Founder at Agentmelt · Last updated Sep 9, 2026
Example
Instead of generating 'The customer wants to upgrade to the Pro plan starting next month,' the agent outputs: { "intent": "upgrade", "plan": "pro", "startDate": "2026-05-01", "confidence": 0.94 }. The application processes this structured output directly without parsing natural language.
Frequently asked questions
- How reliable is structured output from LLMs?
- With schema enforcement (supported by Claude, GPT-4, and others), structured output reliability is 99.9%+. The model is constrained to produce valid JSON matching your schema. Without schema enforcement, models occasionally produce malformed output—which is why constrained decoding is strongly recommended for production agents.