Agentic AI on Google Cloud: Latest Vertex AI, MCP, and Gemini Live Updates

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Agentic AI on Google Cloud (Dec 2025 updates): Managed MCP Servers, Vertex AI Agent Builder Governance, and Native Audio

In December 2025, Google Cloud shipped a set of “agentic AI” building blocks that make a real difference once you leave demos behind and move into production:

  • Fully-managed, remote MCP servers for Google + Google Cloud services (starting with Maps, BigQuery, GCE, and GKE).
  • Stronger tool governance in Vertex AI Agent Builder using Cloud API Registry to curate and control which tools developers can use.
  • Native audio for Gemini Live API (Gemini 2.5 Flash native audio) is now GA for more realistic voice-agent experiences.

This post walks through what’s new, why it matters, and how to apply it in a practical reference architecture.



1) What Google Cloud released (and why it matters)

1.1 Managed MCP servers: “USB-C for tools,” but enterprise-ready

Model Context Protocol (MCP) is emerging as a common standard for connecting models to tools and data. Google Cloud’s December release is important because it shifts the burden from “you host a bunch of fragile MCP servers” to “use fully-managed, remote MCP servers” backed by Google’s API infrastructure.

Instead of packaging local servers per tool and dealing with upgrades, auth, and reliability yourself, you can connect agents (or MCP clients like Gemini CLI) to Google-managed endpoints that expose Google services as structured, discoverable tools.

1.2 Governance in Vertex AI Agent Builder: curated tools at org scale

The second big move is governance: Vertex AI Agent Builder now integrates with Cloud API Registry so administrators can curate an approved tool catalog for the organization, while developers consume those tools in a controlled way inside the Agent Builder experience.

This is the difference between:

  • Prototype mode: “My agent can call anything.”
  • Production mode: “My agent can only call vetted tools, with IAM controls, audit logs, and predictable behavior.”

1.3 Agent Designer preview: faster iteration for agent flows

Vertex AI Agent Builder also introduced Agent Designer (preview): a low-code visual designer in the Cloud Console for designing and testing agent behavior.

1.4 Native audio is GA for Gemini Live API

Voice agents get much more compelling when audio is handled natively (rather than stitching speech-to-text + text LLM + text-to-speech). In December, Gemini 2.5 Flash with Gemini Live API Native Audio became generally available, with features like improved voice quality/adaptability, proactive audio, and affective dialog.

2) Reference architecture: a governed, tool-using agent

Here’s a practical architecture you can use for many enterprise agentic apps: a Gemini-powered agent that can query BigQuery for internal facts and use Google Maps data for real-world grounding—while keeping everything governed. 


User / App
   |
   v
Agent (Gemini on Vertex AI)
   |
   +--> Tool selection + planning
   |
   v
Managed MCP Servers (Google services)
   |            |
   |            +-- BigQuery MCP (queries stay in-place)
   |
   +-- Maps MCP (grounding + routing)
   
Governance + Security
- Cloud API Registry (approved tools catalog)
- IAM (who can call which tool)
- Audit logs (observability)
- Model Armor (protection against agentic threats)

3) Implementation playbook (high-signal steps)

Step A — Decide what “tools” your agent is allowed to use

  • Start small (1–3 tools): e.g., BigQuery (read-only datasets), Maps (routing/places), and optionally one “action tool” (ticket creation, email draft, etc.).
  • Define allowed operations per tool (e.g., BigQuery: SELECT-only; no DDL/DML).
  • Design a human-in-the-loop gate for risky actions (create/modify/delete, spend money, external calls).

Step B — Use Cloud API Registry to curate approved tools

With Cloud API Registry integrated into Vertex AI Agent Builder, admins can publish a vetted catalog of tools developers can use. This is where you encode “what good looks like” for tool access at your org.

Practical tips:

  • Create separate catalogs for dev, staging, and prod.
  • Offer “safe defaults” (read-only tools, constrained scopes).
  • Version tools like APIs: breaking changes should require explicit opt-in.

Step C — Lock down access with IAM and logging

  • Grant tool permissions to a service account used by the agent runtime (not to individual developers).
  • Enable audit logging for tool calls so you can answer: “Who/what agent did what, and when?”
  • Adopt a “deny by default” posture for tools that can mutate state.

Step D — Connect your agent to managed MCP servers

At a high level, your MCP client (agent runtime, CLI, or framework) will register MCP servers as tool backends. The exact config varies by client, but conceptually it looks like this: 


{
  "mcpServers": {
    "bigquery": {
      "transport": "remote",
      "endpoint": "<google-managed-bigquery-mcp-endpoint>",
      "auth": "google_iam"
    },
    "maps": {
      "transport": "remote",
      "endpoint": "<google-managed-maps-mcp-endpoint>",
      "auth": "google_iam"
    }
  }
}

Once registered, the agent can discover and call tools using MCP’s structured interface (instead of fragile prompt-based “call this REST API” patterns).

Step E — Build the agent experience (console-first or code-first)

  • Console-first: Use Agent Designer (preview) to iterate quickly on flows, tool usage policies, and prompt scaffolding.
  • Code-first: Keep prompts, policies, and tool schemas in Git; deploy via CI/CD; treat tool changes like API changes.

4) Adding voice: Gemini Live native audio (GA)

If your agent needs real-time voice, native audio changes the UX dramatically: lower latency and more “conversational” behavior. With Gemini 2.5 Flash native audio now GA for Gemini Live API, you can:

  • Build real-time support agents (triage + handoff)
  • Create voice copilots for field teams (hands-free workflows)
  • Enable proactive voice interactions (when it’s safe + appropriate)

Production note: treat voice as a privileged channel. Add explicit consent UX, clear session boundaries, and strong logging/redaction policies for sensitive environments.

5) Ops considerations you should not skip

Cost + billing guardrails

  • Expect model usage to be the dominant cost driver; tool calls add overhead but are often worth it for correctness.
  • Watch for upcoming billing changes in agent runtime features (sessions/memory/code execution), and plan budgets accordingly.

Security posture for agentic apps

  • Use IAM-scoped tool access + least privilege.
  • Enable audit logs for tool invocations.
  • Protect against prompt injection / indirect injection with layered defenses (validation, allowlists, and dedicated protections like Model Armor where applicable).

Reliability

  • Design for tool failures: retries, fallbacks, and “I can’t access that right now” UX.
  • Implement response verification for critical paths (e.g., BigQuery query result checks, schema expectations, unit tests for prompts).

Conclusion

The December 2025 updates are a clear signal: Google Cloud is pushing agentic AI from “cool demo” toward “governed, observable, enterprise-grade system.” Managed MCP servers reduce glue code and fragility, Cloud API Registry + Vertex AI Agent Builder improve governance at scale, and Gemini Live native audio unlocks richer real-time experiences.

If you’re building agents in 2026, the winning pattern will look like this: tools + governance + observability (not just a bigger model).

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