Google Vertex AI Agents Provide Managed Agent Infrastructure. The Agents Have No Canonical Schema.
by Nick Clark | Published March 28, 2026
Google Vertex AI Agents provides managed infrastructure for building and deploying AI agents with grounding, tool use, and conversation management on Google Cloud. The managed service handles hosting, scaling, and integration. But Vertex AI agents have no canonical schema. Each agent is a configuration of model parameters, grounding sources, and tools. The structural definition of what an agent is, what fields it must carry, and how governance, memory, and identity relate is absent. The gap is between managed agent infrastructure and a canonical agent definition.
Google Vertex AI's agent infrastructure and grounding capabilities provide enterprise-grade agent hosting. The gap described here is about agent structural definition, not about infrastructure quality.
Grounding without structural identity
Vertex AI agents can be grounded in enterprise data through Google Search, Vertex AI Search, or custom data stores. Grounding improves response quality. But grounding is a capability, not a structural field. There is no schema defining how grounding relates to the agent's identity, memory, or governance. Grounding is a feature of the configuration. It is not a typed field of a canonical agent.
Conversation state without governed memory
Vertex AI agents maintain conversation state across turns. But conversation state is session context, not governed memory. There is no lineage tracking, no governance on state mutations, and no structural validation of memory integrity. The state is what the conversation produced. It is not a governed field of the agent.
What a canonical agent schema provides
A canonical agent schema would give Vertex AI agents structural definition. Each agent would carry typed fields for identity, memory, governance, capabilities, execution state, and lineage. The managed infrastructure would validate agent structure, enforce governance constraints, and maintain lineage. Agents would be interoperable across platforms because all platforms would agree on what an agent structurally is.
