AI-Native Cloud Updates: Gemini 3, Agents, Security

Most infrastructure teams still treat “AI” as a separate product category—something you call from an app, not something that shows up inside your databases, API gateways, and ops consoles.

Google Cloud’s late-2025 release notes tell a different story. The pattern is clear: AI is being embedded into the control plane and the data plane—from Gemini models inside databases (AlloyDB, Cloud SQL, Spanner) to multi-gateway API security governance in Apigee, to agent runtimes and memory services in Vertex AI.

If you’re responsible for cloud strategy, data platforms, or data center modernization, this matters because it changes what “good” architecture looks like in 2026: fewer brittle glue layers, more policy-driven automation, and faster feedback loops between data, apps, and security.

AI is moving into the database layer (and that’s the point)

The big shift: databases are no longer just storage + SQL. They’re becoming places where you generate, search, and reason—close to where the data lives.

Google Cloud’s recent updates underline three practical directions.

Gemini 3 inside AlloyDB: generation where latency matters

AlloyDB for PostgreSQL now supports using Gemini 3.0 Flash (Preview) with generative AI functions such as AI.GENERATE (model name gemini-3-flash-preview). This is more than “SQL can call an LLM.” The architectural implication is that teams can:

• Keep sensitive data in the database boundary longer
• Reduce app-layer prompt plumbing
• Standardize how AI touches data (auditing, permissions, rate controls)

In a data center context, I like this direction because it pushes AI work closer to the systems you already scale, monitor, and harden.

“Data agents” for databases: conversational access, but as a tool

AlloyDB, Cloud SQL (MySQL + PostgreSQL), and Spanner all highlight data agents (Preview) that interact with data using conversational language.

Here’s the important framing: these agents are described as tools to empower applications, not just chat UIs. The best use cases I’ve seen for this style aren’t “let anyone ask anything,” but rather:

• Guided ops: on-call engineers asking “what changed in the last 30 minutes?” with the agent producing a safe, scoped query.
• Customer support tooling: an internal agent that retrieves account state, recent transactions, or eligibility decisions.
• Data product interfaces: controlled natural-language entrypoints to curated datasets.

The win isn’t “natural language.” The win is standardized interaction patterns across teams—especially when turnover, incident load, and tool sprawl are real.

Practical advice: don’t put an LLM directly on prod tables

If you’re evaluating data agents or in-database generation:

1. Create an AI-facing schema (views, row-level security, masked fields). Treat it like a read-only API surface.
2. Add query budgets (timeouts, row limits, concurrency caps). Prevent “helpful” full scans at 2 a.m.
3. Log prompts and tool calls into an analytics sink (BigQuery is the obvious choice here). You can’t govern what you can’t measure.

This is where cloud infrastructure AI becomes real: not model choice—guardrails and repeatability.

Vertex AI Agent Engine is becoming an operational runtime (not a demo)

A lot of “agent” chatter online is still prototype-heavy. What stands out in these updates is that Google is hardening the parts teams actually need to run agents in production.

Sessions, Memory Bank, and pricing signals

Vertex AI Agent Engine Sessions and Memory Bank are now Generally Available, and pricing changes are explicit: runtime pricing was lowered, and Sessions/Memory Bank/Code Execution start charging on January 28, 2026.

That date matters for planning. If you’re piloting agents in Q4 and planning production in Q1, you should forecast costs now—especially if your design assumes long-lived sessions or heavy memory retrieval.

Regions are expanding (latency + data residency)

Agent Engine availability expanded to regions including Zurich, Milan, Hong Kong, Seoul, Jakarta, Toronto, and São Paulo.

This isn’t just convenience. It’s what makes AI workload management feasible in regulated environments:

• Lower latency for interactive agent workflows
• More options to keep data in-region
• Better alignment with enterprise network boundaries

What “agent observability” should mean in 2026

Google is also pushing observability features (sessions, traces, logs, events) and console-based playground testing.

My opinion: agent observability needs to look more like SRE telemetry than chatbot analytics. You should be able to answer:

• Which tools were called, in what sequence, and with what parameters?
• What was the model’s response latency distribution (p50/p95/p99)?
• What percent of calls were blocked by safety/security filters?
• Which prompts correlate with incidents, cost spikes, or bad outputs?

If your agent platform can’t answer those with first-class tooling, you’ll end up building a shadow monitoring stack.

API governance is catching up to multi-gateway reality

Most enterprises don’t run a single API gateway anymore. They have a mix: legacy edge gateways, hybrid deployments, business-unit projects, and inconsistent policies.

Google’s Apigee updates are a direct response: centralized security governance across multiple gateways via API hub.

Advanced API Security for multi-gateway projects

Apigee Advanced API Security can now centrally manage security posture across:

• Apigee X
• Apigee hybrid
• Apigee Edge Public Cloud

Key capabilities called out:

• Unified risk assessment across projects/environments/gateways
• Custom security profiles applied consistently

This is the cloud-infrastructure AI story applied to governance: fewer one-off dashboards, more consistent controls.

Risk Assessment v2 and AI-focused policies

Risk Assessment v2 is GA, with support for:

• VerifyIAM
• AI policies like SanitizeUserPrompt, SanitizeModelResponse, SemanticCacheLookup

That list is a tell. Security teams are now being asked to govern not only endpoints and auth, but also:

• Prompt injection and unsafe input
• Data leakage in model outputs
• Cache behavior that can expose cross-tenant or cross-user data

If your org is shipping agentic apps, API security and AI security are merging. Pretending they’re separate is how you get incidents.

Action item: treat AI policies as “API contracts”

If you adopt prompt/response sanitization policies at the gateway layer, document them like contracts:

• What gets blocked vs rewritten?
• What is logged and where?
• What is the escalation path when sanitization breaks a workflow?

Otherwise you’ll get the worst outcome: a security control that developers route around.

Infrastructure capacity planning is shifting toward AI workloads

A quiet thread running through these notes is that AI workloads are forcing more sophisticated resource planning.

Future reservations for GPUs/TPUs (calendar mode)

Compute Engine now supports future reservation requests in calendar mode (GA) for GPUs, TPUs, and H4D resources—up to 90 days.

This is a practical response to scarcity and scheduling realities. If you’re doing fine-tuning, evaluation runs, or batch inference, calendar-mode reservations help you avoid the “we can’t get capacity” surprise.

Sole-tenancy support for GPU machine types

Sole-tenant nodes now support multiple GPU machine types (A2 Ultra/Mega/High and A3 Mega/High).

This matters for organizations that need:

• Compliance-driven isolation
• Predictable performance for AI training
• Tight control of noisy neighbor risks

Known issues are part of the ops plan

There’s also a note: A4 VMs with NVIDIA B200 GPUs might experience interruptions due to firmware, with a recommendation to reset GPUs at least every 60 days.

This is the unglamorous side of AI in data centers: you need runbooks that include firmware realities, not just Kubernetes YAML.

The less flashy updates that still change reliability and security

A few “small” changes in release notes can have outsized operational impact.

Load balancers now reject non-compliant HTTP methods earlier

Requests with methods not compliant with RFC 9110 (Section 5.6.2) are now rejected by Google Front End before reaching certain load balancers.

Net effect: you may see slightly lower backend error rates, but you should also:

• Validate any clients that use unusual HTTP methods
• Confirm monitoring alerts don’t rely on the old error patterns

Single-tenant Cloud HSM is GA

Cloud KMS Single-tenant Cloud HSM is now GA in several regions.

If you handle regulated workloads, the key detail is operational: quorum approval with 2FA using keys secured outside Google Cloud. That’s a workflow change, not just a checkbox.

Enhanced backups are getting more serious

Cloud SQL enhanced backups are GA, with centralized backup management and point-in-time recovery after instance deletion.

That last clause is the one to care about. It’s the difference between “oops” being an outage and “oops” being a ticket.

How to turn these updates into an actionable 2026 roadmap

If you’re building an AI-forward cloud platform (or modernizing a data center footprint), here’s how I’d sequence adoption.

1. Start with governance, not agents

   • Define what data is allowed for AI use
   • Set logging/retention rules for prompts and tool calls
   • Decide where sanitization happens (gateway, app, model layer)

2. Pilot data agents in one domain

   • Choose a narrow, high-value workflow (support, ops, finance ops)
   • Build an AI-facing schema
   • Measure: latency, cost, accuracy, incident rate

3. Standardize multi-gateway security posture

   • Inventory APIs and gateways
   • Adopt unified risk scoring
   • Roll out AI-oriented policies where they protect real workflows

4. Treat AI capacity like a first-class dependency

   • Use future reservations for planned runs
   • Decide when sole-tenancy is required
   • Add hardware/driver/firmware checks to operational readiness

A good 2026 cloud strategy won’t ask, “Where do we add AI?” It will ask, “Which layers of our infrastructure should become AI-native so we can run faster with less risk?”

Where this is headed next

Google Cloud’s release notes read like a roadmap for AI in cloud computing and data centers: intelligence moving closer to data, security controls adapting to agentic traffic, and resource planning shifting to GPU/TPU realities.

If you’re making platform decisions for next year, the question isn’t whether you’ll use generative models. You will. The real question is whether your organization will run them as scattered app features—or as governed, observable, infrastructure-grade capabilities that your whole stack can rely on.

If you’re mapping this into your environment and want a second set of eyes, a useful next step is to list your top three AI workloads (agentic apps, analytics copilots, internal tooling) and trace where they touch data, APIs, and identity. The gaps usually show up fast.