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AI Assets

AI Assets extend the catalog to the systems that build, run, and govern artificial intelligence workloads. The application programming interface (API) models eight resources across five areas. They cover large language models (LLMs), AI agents, and Model Context Protocol (MCP) servers. They also cover AI applications and prompt templates.

LLMs

Catalog LLM services and the models hosted by them.

AI Agents

Model cataloged agents, invoke AI Studio agents, and record executions.

MCP

Catalog MCP services and servers, including their tools, resources, and prompts.

AI Applications

Govern chatbots, agents, and custom AI applications. Cover copilots, assistants, and retrieval-augmented generation (RAG) systems.

Prompt Templates

Version and govern reusable prompts, variables, examples, and evaluation metrics.

Resource Model

Catalog an AI agent as an AI Application whose applicationType is Agent or MultiAgent. AgentExecution stores runtime observations; it isn’t the agent definition. The separate AI SDK manages executable AI Studio agents.

SDK Coverage

The v2.0 core software development kits (SDKs) don’t expose every AI resource through the same interface. The examples in this section use only confirmed client paths. The separate AI SDK supports AI Studio agent management and invocation in Python, TypeScript, Java, and the command-line interface. It doesn’t replace the catalog REST resources listed above.

Common Behavior

LLM services, LLM models, MCP services, MCP servers, AI applications, and prompt templates support standard list, retrieve, create, create-or-update, patch, version, soft-delete, hard-delete, and restore operations. Agent and MCP executions use time-series APIs with create, retrieve, list, and delete operations. Authenticate every request with a bearer token. For setup details, see Authentication.