# Structured output

Model output constrained to a declared machine-readable schema so software can parse and validate its shape.

technical-term · Harness & runtime · maturing · Reviewed 2026-08-09

## Definition

Structured output makes a probabilistic model produce data against a defined schema, improving interoperability and reducing parsing ambiguity. Schema validity establishes shape—not factual correctness, business validity, authorization, or safe effect.

Schema-constrained generation is widely supported, though guarantees and failure handling differ across model APIs.

## Why it matters

Typed boundaries make model decisions easier to inspect, test, reject, and route.

Semantic validation and policy evaluation must still occur in trusted code.

## System anatomy

- **Schema:** Allowed fields, types, enumerations, and required values.
- **Validation:** Trusted checks for structure and domain constraints.
- **Disposition:** Accept, repair, reject, or escalate invalid output.



## Important distinctions

- **Correct output:** A response can satisfy the schema while containing false or unsafe values.
- **Tool authorization:** Typed arguments make validation possible but do not grant permission to execute.



## Implementation signals

- Keep schemas narrow and versioned
- Validate domain invariants after parsing
- Fail closed for consequential actions

## Failure modes

- Automatic repair changes meaning without review
- Free-form strings smuggle unvalidated instructions into typed fields



## Related knowledge

- [Tool calling](https://aisdlc.ai/agentic-engineering/tool-calling) — A model-mediated interface in which the system selects a named function and proposes structured arguments for software to validate and execute.
- [Agentic workflow](https://aisdlc.ai/agentic-engineering/agentic-workflow) — A model-enabled process whose major control path is defined in code, even when models perform individual steps.
- [Runtime policy enforcement](https://aisdlc.ai/agentic-engineering/runtime-policy-enforcement) — The pre-action decision and enforcement layer that evaluates identity, purpose, risk tier, tool, resource, data class, limits, approval state, and current evidence before allowing an agent action.

## Sources and further study

- [OpenAI — A practical guide to building agents](https://openai.com/business/guides-and-resources/a-practical-guide-to-building-ai-agents/) — Defines a practical agent around a model, tools, instructions, and a run loop that continues until an exit condition is reached. **Use:** Primary definition. **Limitation:** The guide is first-party product guidance and a simplified starting architecture, not a complete regulated-enterprise control model.
- [Model Context Protocol — Model Context Protocol architecture overview](https://modelcontextprotocol.io/docs/2026-07-28/learn/architecture) — Documents the host, client, server, capability-negotiation, and message layers used to connect AI applications with context and tools. **Use:** Standard or protocol. **Limitation:** Protocol connectivity and capability discovery do not confer business authority, validate tool output, or make a server trustworthy.

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This library synthesizes cited research, standards, official documentation, and clearly attributed practitioner perspectives. Maturity describes the state of a concept—not vendor endorsement, production readiness, or permission to deploy. Benchmarks and demonstrations do not replace use-case evaluation, governed controls, independent verification, or named human release authority.