Gemini Enterprise Agent Platform: A Developer’s First Look at Next ’26’s AI Wave

RISHAV ROY — Wed, 29 Apr 2026 16:17:09 +0000

This is a submission for the Google Cloud NEXT Writing Challenge

Google Cloud’s new Gemini Enterprise Agent Platform aims to unify AI development for “agentic” applications. At Next ’26, Google announced that Vertex AI is evolving into this one-stop platform – “a comprehensive platform to build, scale, govern, and optimize agents”. In practice, that means moving beyond calling individual models or APIs to designing full agents that can reason, act, and even collaborate. As a developer, this stood out: the promise is to remove much of the boilerplate and friction we’ve dealt with.

What’s New with Agent Platform

Gemini Enterprise Agent Platform is not just a new name, it’s a redesigned AI toolkit. In Thomas Kurian’s keynote he framed it as “the evolution of Vertex AI,” bringing together all model- and agent-building capabilities with new orchestration and governance features. Key highlights include:

Unified Agent Development: Two parallel workflows – a visual Agent Studio (low-code drag-and-drop) and a code-first Agent Development Kit (ADK) – let anyone build agents. By default I use ADK for full control; the console now cleanly offers either path. In code, an agent is just a Python object with a name, model, instruction, and a list of Tool functions (for example, a lookup_order() API) – all in ~15 lines of logic, no boilerplate or YAML needed.
Serverless Agent Runtime: Once an agent is ready, it runs on a managed runtime (built on Cloud Run). It supports long-running agents with persistent state (backed by a “Memory Bank”). In my trial, deploying an agent was a one-liner gcloud command, and within minutes I had an endpoint. Best of all, this uses Cloud Run’s free tier (first 180K vCPU-seconds/month), so small projects can run at virtually no cost.
Built-in Observability & Control: The platform includes Agent Observability – a dashboard that traces the full reasoning chain (every tool call, model invocation, decision) as the agent runs. It’s literally a debugger for agents: I could click through each step of my agent’s answer to see how it decided to call lookup_order. Under the hood it also provides identity/registry/gateway features to track and secure agents, as well as simulation/evaluation tools for QA.
Model Ecosystem: Agent Platform gives first-class access to Google’s top models (e.g. Gemini 3.1 Pro, Gemini 3.1 Flash/Nano Banana 2, Lyria 3) and many others (Anthropic’s Claude, open LLaMA variants, etc.) . I could easily switch my demo agent’s model (from Gemini 2.0-Flash to Gemini 3.1 Pro) with just a parameter change. The platform also plugs into “Model Garden” for testing/tuning any model.
Multi-Agent Protocol (A2A): Google introduced an Agent-to-Agent (A2A) protocol. In short, multiple agents can now discover and communicate directly on a standard protocol (akin to “HTTP for agents”). I briefly chained my support agent with a simple “refund processor” agent and the built-in A2A routing handled the interaction. No extra API glue was needed – it worked out of the box.

Together, these features show Google aiming for an “agentic enterprise” where AI pieces plug together seamlessly. As Kurian put it, Agent Platform is the foundation of that vision, handling “every aspect of your agents” in one place
.

Hands-On: Building a Simple Agent

Rather than just read docs, I immediately tried building a trivial “customer support” agent. The experience was shockingly smooth:

Install SDK: In my terminal I ran:

pip install google-cloud-agent-platform google-adk

That was it – no extra gcloud projects or API keys at this point. The new SDK bundles everything so I could code right away.

Write the agent: In ~15 lines of Python I defined two simple tool functions (lookup_order(order_id) and escalate_ticket(issue, name)) and created an Agent instance with those tools and a short instruction. For example:

from google.adk import Agent, Tool

def lookup_order(order_id): 
    orders = {"ORD123": {"status": "shipped", "eta": "Apr 30"}}
    return orders.get(order_id, {"error":"Not found"})

def escalate_ticket(issue, name):
    return {"ticket_id": "TKT1001", "status": "escalated"}

support_agent = Agent(
    name="support-assistant",
    model="gemini-3.1-pro",
    instruction="You are a helpful customer support assistant.",
    tools=[
        Tool(name="lookup_order", function=lookup_order),
        Tool(name="escalate_ticket", function=escalate_ticket),
    ]
)

response = support_agent.run("Where is my order ORD123?")
print(response.text)

By coincidence, this example is almost identical to one shared by a fellow dev – they emphasize that it was “15 lines of actual logic. No boilerplate”. And indeed, it ran on the first try!

Test locally: The call support_agent.run(...) returned a response text right away. Behind the scenes it invoked Gemini, parsed the JSON from lookup_order(), and composed an answer. Seeing it work immediately was delightful – I could focus on what the agent should do, not wiring it up.

Deploy to runtime. To make it production-ready, I used the new gcloud commands:

gcloud alpha agent-platform runtimes agents deploy support-assistant \
    --region=us-central1 --display-name="Support Agent" --entry-point=agent.py

About 2 minutes later it was live. From there I could use the Google Cloud console or API to query the endpoint from anywhere. This is handled by “Agent Runtime,” which is just Cloud Run underneath – hence the free tier applies
and I could keep experimenting without incurring charges.

Overall, the Developer Experience feels frictionless. The SDK setup literally took seconds (no juggling multiple libraries or credentials). Writing the agent code was focused only on logic and language prompts. Even deploying was a simple CLI – nothing like wrestling with Kubernetes or servers. As one reviewer noted, this is the kind of UX that makes you say “Wait, that worked on the first try?”
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Pros and Caveats

What works well: I love that almost everything is managed. Out-of-the-box observability means I can debug agent behaviors visually. The platform scales agents under the hood (honestly, I didn’t have to think about scaling). The free tier usage is real – I got roughly 180k CPU-seconds free per month (same as Cloud Run) – so a solo developer or small project can do a lot at no cost. Also, having a unified place for models and data connections is powerful: the platform even promises a “Knowledge Catalog” and cross-cloud lakehouse to ground agents on data (though I didn’t test those yet).

Challenges: It’s not all perfect. The rebrand from Vertex AI was a bit jarring – documentation and tutorials are still catching up. For instance, Google quietly said Vertex AI services will move exclusively to Agent Platform going forward, and Vertex libraries after June 2026 won’t be updated. That means if you have legacy Vertex code, you’ll need to migrate soon. Some docs still refer to old names, so you have to mentally translate things.

The new Agent Designer (low-code) is cool for quick demos, but in practice it’s limited to simple flows. I found complex logic still pushes me into ADK code. And while the platform gave me power, certain enterprise aspects feel “beta”: there’s not yet clear CI/CD guidance, and advanced features (like fine-grained IAM or model governance) have thin docs. For example, setting up ModelArmor or grounding agents in private data is promised, but I didn’t see step-by-step guides yet.

Finally, there’s always the question of cost and opacity: we traded compute management for model usage. Google’s new infrastructure (like 8th-gen TPUs and custom chips) means we’ll need to watch usage. The underlying pricing (beyond free tier) can still be confusing for new agent workloads.

Why This Matters to Us

Stepping back, Gemini Enterprise Agent Platform signals a shift in AI dev. We’re moving from the “model era” to an “agent era,” as one expert put it. It’s no longer about which model, but how we orchestrate them. For developers, this means treating AI systems more like microservices or workflows: we design agents with tools, loops, memory, and governance. The platform makes that accessible.

My takeaway: The glue (plumbing) is being normalized. If your app needs smart assistants, RPA, or automated workflows, this platform could be a game-changer. You can prototype end-to-end without building a lot of infrastructure from scratch. The developer velocity is real – I spent minutes, not days, on my test agent. In practical terms:

Do try it if you can: Use the free credits and free tier to spin up an agent, even a toy one. You’ll see how straightforward it is to connect LLM reasoning with your own code and data.
Plan migration if you’re on Vertex: Google is basically telling us Vertex AI as a standalone thing is gone. Learn the new ADK and workspace; think about how to re-write existing pipelines on Agent Platform.
Don’t expect magic no-code for all: The no-code studio is neat, but you’ll likely need to code to get complex behavior. Treat Agent Designer as a helper, not a full replacement for developers.

In short, Google Cloud NEXT ’26 didn’t just drop new APIs – it dropped a developer experience. The Gemini Enterprise Agent Platform is an ambitious bet that AI tooling belongs in one place. If that idea resonates with you, it’s worth exploring firsthand. It might feel a bit raw at first, but it’s the clearest signal yet of where cloud AI is headed: not just smarter models, but smarter ways of building with them.

DEV Community: RISHAV ROY

Gemini Enterprise Agent Platform: A Developer’s First Look at Next ’26’s AI Wave

What’s New with Agent Platform

Hands-On: Building a Simple Agent

Pros and Caveats

Why This Matters to Us