Google’s Gemini Enterprise Agent Platform: What’s New and Why It Matters

AI agents are quickly moving from simple chatbots to complex systems that can run for days, talk to multiple tools, and make decisions on their own. To keep up with this shift, Google has replaced Vertex AI with a completely rebuilt stack: the Gemini Enterprise Agent Platform.

This platform is designed to handle the full lifecycle of AI agents in one place—building, scaling, governing, and optimizing—so teams don’t have to glue together separate tools and dashboards just to keep agents running reliably.

From Vertex AI to Gemini Enterprise Agent Platform

Vertex AI was built for a simpler world: you sent a model a prompt and got a result. That worked fine when you were just calling an LLM for single tasks. But as agents started coordinating across multiple systems, running long workflows, and talking to each other, the cracks started to show.

Security and access control were hard to manage. Tracking what each agent actually did was painful. Debugging meant digging through logs by hand. Google’s answer is the Gemini Enterprise Agent Platform, announced on April 26, 2026, as the new standard for agent-based applications on Google Cloud.

All new Vertex AI services and roadmap updates will now ship through this platform, so if you’re already using Vertex AI, this is effectively your upgrade path. For a more hands-on, developer-focused breakdown, you can also check out this practical guide to Gemini Enterprise Agent Platform.

Building Agents: Studio, ADK, and Templates

The platform gives you two main ways to build agents, depending on how technical your team is and how complex your workflows are.

Agent Studio: Low-Code for Fast Prototypes

Agent Studio is a visual, low-code interface for anyone who needs to design and deploy agents without writing a lot of code. You can start from a simple prompt, define how the agent should behave, connect it to data and tools, and deploy—all inside one environment.

When you hit the limits of low-code, you don’t have to start over. You can export the logic into the code-first toolkit and keep building from there.

Agent Development Kit (ADK): Code-First for Serious Systems

The Agent Development Kit is where large-scale, production-grade agents are built. Google reports that more than 6 trillion tokens are processed monthly on Gemini models through ADK, which gives a sense of the scale it’s already handling.

ADK now supports a graph-based framework. Instead of one giant agent trying to do everything, you can design a network of specialized sub-agents that delegate tasks to each other. For example, you might have separate agents for data retrieval, analysis, compliance checks, and user communication, all coordinated as a team.

For critical workflows—like compliance or approvals—you can lock these agents into deterministic paths. That means they must follow specific, predefined steps every time, reducing the risk of unexpected behavior in sensitive processes.

Agent Garden and Native Integrations

To avoid starting from scratch, the platform includes Agent Garden, a library of pre-built agent templates. These cover common use cases such as code modernization, financial analysis, and invoice processing. You can grab a template, customize it, and get to a working prototype much faster.

There are also native ecosystem integrations that provide a plug-and-play way to connect agents to your internal data and tools. Instead of writing custom connector code for every system, you can wire agents into your stack with a consistent architecture.

Scaling Agents: Long-Running Workflows and Memory

Once your agents are built, the next challenge is running them at scale. Google has rebuilt the agent runtime to support both high performance and long-lived workflows.

Faster Startup and Long-Running Agents

The platform now offers sub-second cold starts, so agents spin up almost instantly, even when they haven’t been used recently. More importantly, you can deploy agents that run autonomously for days at a time.

This opens up workflows that used to require constant human supervision, such as:

• Sales prospecting sequences that unfold over a week

• Research agents that monitor topics over multiple days and summarize new findings

• Back-office processes that run end-to-end without manual babysitting

Memory Bank: Real Long-Term Memory for Agents

Most AI agents only remember what happens in a single session. Once the conversation ends, the context disappears and the next interaction starts from zero. Memory Bank changes this by giving agents persistent, long-term memory across sessions.

Memory Bank automatically generates and curates memories from conversations and interactions. That allows agents to remember who a user is, what they’ve asked before, and what their preferences are—over time, not just in a single chat.

Real-world examples include:

• Gurunavi, a restaurant discovery app, uses Memory Bank so the agent can remember user preferences and behavior. Instead of waiting for users to ask, the agent can proactively suggest restaurants that match their tastes, with projected user satisfaction improvements of 30% or more.

• Payhawk built a financial controller agent that remembers expense habits and can auto-submit expenses based on past patterns. This has cut expense submission time by more than 50%.

This isn’t just a nicer chat experience; it’s persistent memory directly improving business workflows and user satisfaction.

Agent Sandbox for Safe Automation

When agents need to execute code or automate tasks in the browser, security becomes a major concern. The platform addresses this with Agent Sandbox, a hardened, isolated environment where agents can run scripts and perform browser-based automation without touching your core systems.

Everything happens inside secure containers, so if something goes wrong, it’s contained. This makes it safer to let agents interact with external websites, APIs, and untrusted content.

Governance and Security for Enterprise-Scale Agents

As organizations build more agents, a new problem appears: agent sprawl. Different teams spin up their own agents, partners add theirs, and suddenly no one has a clear picture of what’s running or how it’s behaving.

The Gemini Enterprise Agent Platform tackles this with a dedicated governance layer built around three core tools.

Agent Identity: Every Agent Has a Proven Identity

Each agent gets a unique cryptographic identity. Every action it takes is logged and tied back to that ID. This gives you a complete audit trail: you can see exactly what an agent did, when it did it, and why.

For regulated industries or sensitive workflows, this level of traceability is essential for compliance and incident response.

Agent Registry: A Single Source of Truth

Agent Registry is a central directory of every approved agent, tool, and skill inside your organization. Only governed, vetted assets appear here, so teams can safely discover and reuse existing agents without worrying they’re using something experimental or unapproved.

This helps reduce duplication, keeps standards consistent, and gives security and compliance teams a clear inventory of what’s in production.

Agent Gateway, Model Armor, and Threat Detection

Agent Gateway acts like air traffic control for your entire agent fleet. All traffic between agents and tools flows through it, and it enforces consistent security policies across the board.

Built into this layer is Model Armor, which is designed to protect against prompt injection and data leakage. Combined with anomaly and threat detection, the platform can flag unusual agent behavior in real time.

A dedicated agent security dashboard brings this together, unifying threat detection and risk analysis. If an agent starts acting suspiciously, you can see it quickly and respond before it causes damage.

Testing, Observability, and Continuous Optimization

Launching an agent is only half the job. You also need to know if it’s actually doing what you expect—and how to improve it. The platform includes built-in tools for simulation, evaluation, observability, and automatic optimization.

Agent Simulation and Live Evaluation

Agent Simulation lets you test agents before they go live by running them against synthetic users. The system generates realistic multi-step conversations and scores agents on task success and safety.

Once agents are in production, live agent evaluation continues this process using multi-turn auto-raters. Instead of judging single responses in isolation, these evaluators look at the logic and quality of entire conversations, which is a much higher bar.

Agent Observability and Optimizer

Agent Observability provides full execution traces so you can visually follow the chain of reasoning an agent used to reach a decision. This makes debugging and performance tuning far more practical than sifting through raw logs.

Agent Optimizer then takes it a step further. It automatically clusters failed interactions and suggests refined system instructions to fix them. Instead of manually reading through dozens or hundreds of bad conversations, you get grouped insights and concrete suggestions for improving your agent’s behavior.

If you want a deeper, end-to-end walkthrough of how to go from prototype to production with these tools, take a look at this guide on building real agents with Gemini Enterprise Agent Platform.

Models: One Platform, Many Options

Under the hood, the platform is powered by Model Garden, which gives you access to more than 200 models. That includes Google’s own latest models—such as Gemini 3.1 Pro, Gemini 3.1 Flash Image, and Gemma 4—as well as third-party models like Anthropic’s Claude Opus, Sonnet, and Haiku.

This means you’re not locked into a single model for every task. You can mix and match: use a lightweight Flash model for fast, simple responses, and a more powerful model for complex reasoning or high-stakes decisions. The goal is to pick the right model for each part of your workflow, all inside one platform.

Why This Release Matters

The Gemini Enterprise Agent Platform isn’t just a rename of Vertex AI; it’s a full rebuild aimed at the realities of modern AI agents. The key strengths are:

• End-to-end lifecycle support: Build, scale, govern, and optimize agents in one place.

• Persistent memory: Memory Bank gives agents long-term memory across sessions, enabling smarter, more personalized workflows.

• Enterprise-grade governance: Agent Identity, Registry, and Gateway provide visibility, control, and security at scale.

• Built-in testing and optimization: Simulation, evaluation, observability, and Agent Optimizer close the loop without needing a separate stack.

For teams already on Vertex AI, this is the direction Google is moving everything. For anyone serious about deploying agents in production—especially in regulated or complex environments—the Gemini Enterprise Agent Platform is worth a close look.