#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.

“The AI platform team used Agent Response Schema when an agent moved from search to action, so the team could make responses machine-readable before the agent workflow reached production.”

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.

“The AI platform team used Alignment Response Schema when the assistant needed a safer answer style, so the team could make responses machine-readable before the agent workflow reached production.”

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.

“The AI platform team used Context Response Schema when the context window filled with mixed sources, so the team could make responses machine-readable before the agent workflow reached production.”

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.

“The AI platform team used Evaluation Response Schema when a release candidate failed a reasoning scenario, so the team could make responses machine-readable before the agent workflow reached production.”

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.

“The AI platform team used Guardrail Response Schema when the model tried to include private context, so the team could make responses machine-readable before the agent workflow reached production.”

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.

“The AI platform team used Inference Response Schema when the inference route moved to a faster region, so the team could make responses machine-readable before the agent workflow reached production.”

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.

“The AI platform team used Memory Response Schema when the assistant reused earlier project context, so the team could make responses machine-readable before the agent workflow reached production.”

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.

“The AI platform team used Model Response Schema when the model produced a low-confidence answer, so the team could make responses machine-readable before the agent workflow reached production.”

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.

“The AI platform team used Prompt Response Schema when the prompt changed between releases, so the team could make responses machine-readable before the agent workflow reached production.”

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.

“The AI platform team used RAG Response Schema when the retriever mixed old and new documents, so the team could make responses machine-readable before the agent workflow reached production.”

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.

“The AI platform team used Routing Response Schema when the router selected a cheaper model, so the team could make responses machine-readable before the agent workflow reached production.”

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.

“The AI platform team used Tool Call Response Schema when the assistant requested a protected operation, so the team could make responses machine-readable before the agent workflow reached production.”