#response-schema

Agent Response Schema is a ai output contract that requires model output to match a known structure for tool-using assistant workflows. It uses JSON schemas, validators, retries, and error reporting so teams can make responses machine-readable while keeping evidence, reliability, and public-safe operational boundaries clear.

Alignment Response Schema is a ai output contract that requires model output to match a known structure for model behavior shaping and policy fit. It uses JSON schemas, validators, retries, and error reporting so teams can make responses machine-readable while keeping evidence, reliability, and public-safe operational boundaries clear.

Context Response Schema is a ai output contract that requires model output to match a known structure for runtime memory and retrieved information. It uses JSON schemas, validators, retries, and error reporting so teams can make responses machine-readable while keeping evidence, reliability, and public-safe operational boundaries clear.

Evaluation Response Schema is a ai output contract that requires model output to match a known structure for AI quality and safety testing. It uses JSON schemas, validators, retries, and error reporting so teams can make responses machine-readable while keeping evidence, reliability, and public-safe operational boundaries clear.

Guardrail Response Schema is a ai output contract that requires model output to match a known structure for policy controls around model input and output. It uses JSON schemas, validators, retries, and error reporting so teams can make responses machine-readable while keeping evidence, reliability, and public-safe operational boundaries clear.

Inference Response Schema is a ai output contract that requires model output to match a known structure for model execution for user or system requests. It uses JSON schemas, validators, retries, and error reporting so teams can make responses machine-readable while keeping evidence, reliability, and public-safe operational boundaries clear.

Memory Response Schema is a ai output contract that requires model output to match a known structure for persistent or session-level AI state. It uses JSON schemas, validators, retries, and error reporting so teams can make responses machine-readable while keeping evidence, reliability, and public-safe operational boundaries clear.

Model Response Schema is a ai output contract that requires model output to match a known structure for foundation model behavior and serving. It uses JSON schemas, validators, retries, and error reporting so teams can make responses machine-readable while keeping evidence, reliability, and public-safe operational boundaries clear.

Prompt Response Schema is a ai output contract that requires model output to match a known structure for instructions and context passed to a model. It uses JSON schemas, validators, retries, and error reporting so teams can make responses machine-readable while keeping evidence, reliability, and public-safe operational boundaries clear.

RAG Response Schema is a ai output contract that requires model output to match a known structure for retrieval-augmented generation pipelines. It uses JSON schemas, validators, retries, and error reporting so teams can make responses machine-readable while keeping evidence, reliability, and public-safe operational boundaries clear.

Routing Response Schema is a ai output contract that requires model output to match a known structure for selection among models, tools, and workflows. It uses JSON schemas, validators, retries, and error reporting so teams can make responses machine-readable while keeping evidence, reliability, and public-safe operational boundaries clear.

Tool Call Response Schema is a ai output contract that requires model output to match a known structure for model-triggered calls into software systems. It uses JSON schemas, validators, retries, and error reporting so teams can make responses machine-readable while keeping evidence, reliability, and public-safe operational boundaries clear.