Vertex AI Is Dead, Long Live the Gemini Enterprise Agent Platform: Google's Agentic All-In at Cloud Next '26

There’s a particular kind of meeting that happens inside large tech companies about twice a decade. It usually starts with someone saying, “The brand has become a liability,” and ends with a PowerPoint slide that says “One Platform.” Google had that meeting sometime in early 2026, and on April 22nd in Las Vegas, the rest of the industry found out about it.

Vertex AI — Google’s once-sprawling, sometimes-confusing, undeniably powerful ML platform — is gone. Well, not gone gone. Think of it less as a funeral and more as a name change after a very lucrative marriage. The entity formerly known as Vertex AI is now the Gemini Enterprise Agent Platform, and if you squint at the slides from Cloud Next ‘26, Google is betting this rebrand will be to enterprise agentic AI what AWS EC2 was to cloud compute: the default infrastructure layer everyone defaults to when they stop arguing about it.

Whether that bet pays off is a story still being written. But whether you should care about it right now? That answer is considerably less ambiguous.

What Actually Changed (Beyond the Logo)

Let’s be honest with each other: a lot of enterprise platform rebrands amount to a new color scheme and a fresh marketing deck. This one is different, and the delta matters.

Google didn’t just slap “Gemini” on the Vertex AI console. They folded three previously separate products into a single billing surface and a unified control plane:

• Vertex AI (model serving, training pipelines, MLOps tooling)
• Agentspace (the enterprise search and agent workspace)
• Gemini Code Assist Enterprise tier (the GitHub Copilot competitor)

One login. One console. One bill. For enterprise buyers negotiating cloud contracts, this is actually a big deal — it eliminates the awkward “which Vertex product does this fall under?” conversation that their procurement teams have been having for two years.

But the more interesting change is structural. Google reorganized everything around four verbs: Build, Scale, Govern, Optimize. These aren’t arbitrary marketing pillars. They map directly to the four places where enterprise AI agent projects die in the real world.

Build: Two Paths, One Platform

Google’s agent development story now has two distinct on-ramps depending on how much you like YAML.

The Agent Studio is the low-code visual builder. You can drag-drop agent logic, wire up tools, define triggers, test in a sandbox, and export directly into full-code mode when your requirements inevitably outgrow the canvas. The “export to ADK” button is the part that actually matters — it means you can prototype visually and then hand off to an engineer without starting over.

The Agent Development Kit (ADK) is the serious-engineering path. It’s open-source (Apache 2.0), Python-first, and built around a graph-based orchestration framework — a meaningful upgrade from the linear chain paradigm most practitioners are used to. Each node in the graph can be a sub-agent, a tool call, a conditional branch, or a human-in-the-loop checkpoint. The new graph model handles the kinds of multi-path, recoverable workflows that make production agentic systems actually safe to deploy.

The ADK also ships with a revamped local debugger that lets you replay individual graph nodes with modified inputs — genuinely useful for tracing the “why did it hallucinate here?” problems that make agentic debugging such a joy.

Scale: Agent Engine Gets Fast

If you’ve ever stared at a cold-start time on a cloud function wondering who on earth decided 8 seconds was acceptable, Agent Engine has a message for you: sub-second cold starts, provisioning in seconds, Python SDK or container images.

The revamped Agent Engine (formerly Vertex AI’s managed runtime) is purpose-built for agentic workloads rather than retrofitted from a general serverless container platform. The distinction matters in practice because agents have a fundamentally different traffic profile than traditional microservices — they’re bursty, stateful between steps, and increasingly long-running.

Which brings us to Memory Bank, the genuinely new piece in this release. Memory Bank provides persistent, structured, long-term context across agent sessions. You can now deploy agents that run autonomously for days at a time — the kind of “go do this background research task and come back when it’s done” workflows that previously required you to build your own persistence layer with Redis, Postgres, and a prayer.

Memory Bank supports three memory scopes: session-scoped (within a single execution), user-scoped (across sessions for a specific end user), and global (shared knowledge across all agent instances). The global scope is where things get interesting for enterprise deployments — imagine an agent network where individual agents contribute learnings to a shared knowledge store that improves the whole fleet over time. Google is calling this “collective memory,” and yes, it sounds like something from a sci-fi novel, but the engineering underneath is surprisingly straightforward.

Govern: Finally, the Boring Part Gets Serious

Here’s a number worth sitting with: as of Q1 2026, only 10-14% of enterprise AI agent pilots have reached production at scale. The other 86-90% are stuck in pilot purgatory. The leading causes? Not model capability, not infrastructure cost — governance, security, and auditability.

Google’s answer is Agent Identity and Agent Gateway, two new platform primitives that address the enterprise security gap that’s been quietly killing agentic deployments.

Agent Identity gives every deployed agent a cryptographic identity — a service account with scoped permissions, audit logging, and revocation capabilities. Your compliance team will stop having a small cardiac event when they hear the word “autonomous agent” if you can demonstrate that each agent has an identity, a permission boundary, and a full audit trail.

Agent Gateway is the traffic control layer. Every agent-to-agent call, tool invocation, and external API hit routes through Gateway, which enforces rate limits, applies content policies, logs latency and error rates, and can kill individual agents without taking down an entire multi-agent system. Think of it as an API gateway, but for agents specifically — it understands the agentic call graph rather than just treating everything as generic HTTP traffic.

The Agent Registry completes the governance story. It’s a versioned catalog of all deployed agents in your organization — build metadata, permission sets, performance benchmarks, and dependency graphs. The Registry integrates with your existing CI/CD tooling so you can apply the same release management discipline to agents that you apply to software services. Novel concept, I know.

Model Garden: 200+ Models, Including the Competition

Here’s the detail that generated the most Slack messages in my circles: the Gemini Enterprise Agent Platform’s Model Garden ships with over 200 models, and that list prominently includes Claude Opus 4.7, Claude Sonnet, and Claude Haiku from Anthropic.

Google is explicitly selling access to its primary rival’s models inside its own platform. This is either a remarkably mature “we’ll sell you the best tool for the job” positioning, or it’s a bet that lock-in happens at the infrastructure layer (Agent Engine, Memory Bank, Agent Gateway) rather than the model layer, and therefore giving customers model choice costs Google very little while reducing the “what if Gemini isn’t best for this task?” objection.

The Gemini side of the catalog is headlined by Gemini 3.1 Pro, now generally available on the platform with a 2-million-token context window, native video understanding, and document-level caching that meaningfully reduces costs for long-context enterprise workflows. Gemini 3.1 Flash Image (the model Google whimsically branded “Nano Banana 2” in developer previews) is also available for multimodal agent tasks requiring image generation inline with reasoning.

The open-weight options include Gemma 4 in several sizes and — for teams that need competitive benchmarks on coding and reasoning without a per-token bill — Llama 4 Maverick (400B total parameters, 17B active via MoE), which is available for self-hosted deployments from within the platform.

The Silicon: TPU 8t and TPU 8i

No Google Cloud announcement is complete without a custom silicon reveal, and Cloud Next ‘26 delivered two of them — each optimized for opposite ends of the AI compute lifecycle.

TPU 8t is the training chip. It scales to a superpod of 9,600 TPUs sharing 2 petabytes of high-bandwidth memory through a new Inter-Chip Interconnect (ICI) architecture. Performance is 3x that of the previous Ironwood generation at 2x better performance per watt. For enterprises fine-tuning very large models (70B+) or running distributed training runs that previously required multi-week cloud reservations, TPU 8t changes the math on what’s feasible inside a cloud budget.

TPU 8i is the inference chip, and it’s designed around the agentic use case specifically. The Boardfly topology connects 1,152 TPUs in a single pod with significantly lower cross-chip latency than a traditional mesh. On-chip SRAM is 3x higher than the previous generation, and a dedicated Collectives Acceleration Engine handles the all-reduce operations that dominate multi-agent inference workloads. The headline number: 80% better performance per dollar for inference versus the prior generation. For teams running thousands of agent calls per second, that’s not a marginal improvement — it’s a budget restructuring event.

What This Means If You’re Running on Vertex AI Today

The short answer: nothing breaks. Google has committed to backward compatibility, and existing Vertex AI APIs remain functional under the new platform. The migration path is a console rebrand plus some new capabilities you can adopt at your own pace.

The slightly longer answer: if you’re in the middle of evaluating whether to build your next agentic system on Vertex AI, AWS Bedrock Agents, Azure AI Foundry, or something self-hosted — this announcement changes the comparison. Google has gone from “powerful but fragmented” to “unified with a coherent abstraction layer.”

The places where Google still has work to do: Agent Studio is genuinely early-stage compared to what Microsoft is shipping in Copilot Studio, the Memory Bank global-scope feature is in preview rather than GA, and the A2A (Agent-to-Agent) interoperability story — which Google co-authored and has been positioning as an open standard — is still negotiating adoption across the industry rather than being a settled infrastructure layer.

The Competitive Context in Two Paragraphs

OpenAI is doing its own version of this consolidation play. Codex is being positioned as the enterprise agentic coding layer, OpenAI launched Codex Labs with implementation partners (Accenture, PwC, Infosys), and weekly active users crossed 4 million in early April. The key difference: OpenAI is betting on a model-centric enterprise motion, where you pay for model capability and OpenAI provides the surrounding infrastructure. Google is betting on an infrastructure-centric motion, where the platform is the stickiness and the model is one commodity among 200.

Microsoft, meanwhile, is threading a needle — it has both the Azure AI Foundry (infrastructure play) and Copilot Studio (low-code play) and GitHub Copilot (developer tool play), which means it’s essentially running all three strategies simultaneously. Which is either the mark of a company that serves multiple customer segments, or the mark of a company that hasn’t picked a strategy. Depending on the quarter, it looks like both.

What to Watch

As this story develops over the next 30–90 days, keep an eye on a few specific signals:

First, GA dates for Memory Bank global scope and Agent Gateway — both are currently in preview, and enterprise procurement decisions will accelerate when they hit general availability with SLAs attached.

Second, independent benchmark comparisons of TPU 8i inference costs versus Nvidia H200/H100 for inference workloads. Google’s “80% better perf/dollar” claim is measured against their own prior generation, which is not the same as beating Nvidia on the workloads customers actually run.

Third, whether the Claude/Llama/Gemma model choice inside the platform drives genuine stickiness or whether sophisticated customers use the model flexibility as a negotiating lever to avoid long-term Google contracts. The enterprise platform business is a long game.

And finally, watch the ADK adoption curve. Google’s open-source frameworks (TensorFlow, JAX, Kubeflow) have a complicated history of being heavily used but losing mindshare to less-powerful alternatives with better developer experience. If ADK becomes the default agentic orchestration layer the way LangChain once was, that’s a strategic win for Google that outlasts any single model generation.

The agentic infrastructure race is not over. But Google just showed up with a real contender.

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Sources

• Google Cloud Blog: Introducing Gemini Enterprise Agent Platform
• SiliconANGLE: Google brings agentic development under one roof
• Google Blog: Eighth generation TPUs for the agentic era
• Virtualization Review: Cloud Next ‘26 wrap-up
• The Next Web: Google Cloud Next 2026 — A2A, Workspace Studio, full-stack bet
• Google Cloud: Next 2026 wrap-up
• AIwire / HPCwire: Google Unveils Gemini Enterprise Agent Platform
• TechWire Asia: Google Cloud introduces AI agent platform and new TPUs
• Google Cloud: ADK Documentation
• UI Bakery: Vertex AI Agent Builder 2026 guide