DEV Community: Shubham Verma

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Shubham Verma — Wed, 20 May 2026 11:35:34 +0000

Google I/O Challenge submission

Shubham Verma

May 20

I/O 2026: Forget the Glasses. Let's Talk About the AI Context Nightmare Google Just Fixed.

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I/O 2026: Forget the Glasses. Let's Talk About the AI Context Nightmare Google Just Fixed.

Shubham Verma — Wed, 20 May 2026 11:35:02 +0000

I'll be honest: if you watched the Google I/O 2026 keynotes, you probably saw a lot of flashy consumer tech. There was a highly choreographed demo where Gemini used Android XR smart glasses to identify things in the room and play Charli XCX as entrance music.

As a consumer, that’s neat. As a developer? I don't care. To me, it felt like a glorified Bluetooth microphone that just connects to your phone to do the actual heavy lifting.

What I do care about is the heavy lifting. I care about the backend infrastructure required to make these autonomous "agentic" workflows actually function in the real world. For the past year, building an AI agent has been an absolute architectural nightmare. If you wanted an agent to execute Python code safely or search the web, you had to manually provision secure sandboxes, manage complex execution loops, and constantly shuffle massive arrays of message history back and forth to the LLM just to maintain the conversational context.

That’s why the most underrated and genuinely exciting announcement from Google I/O 2026 wasn't a piece of hardware or a shiny new IDE. It was a backend structural shift: Managed Agents and the Interactions API.

Here is a first-look guide into why this update fundamentally changes how we build with AI, and why I believe it's the single most important tool Google shipped this year.

The Overlooked Gem: Managed Agents in the Gemini API

Historically, an LLM was just a stateless text generator. If you wanted it to act like an "agent"—meaning it could plan, use tools, and execute code—you had to build a massive orchestration layer around it.

Google is finally abstracting that away. With Managed Agents in the Gemini API, you can now spin up a fully provisioned agent powered by the new Antigravity harness (running on the lightning-fast Gemini 3.5 Flash model) with a single API call.

What does that actually mean? It means Google hosts an isolated, ephemeral Linux cloud sandbox for your agent. Your agent can autonomously reason, execute code, manage files, and browse the live internet without you having to configure a single Docker container or AWS Lambda function.

Getting Started: A Hands-On Look

Let’s look at how ridiculously simple the implementation is using the new Python SDK (google-genai).

If I want to spin up an agent to do some deep data analysis, all I have to do is tell the API that I want a "remote" environment:

Python

from google import genai  

client = genai.Client()  
interaction = client.interactions.create(  
    agent="antigravity-preview-05-2026",  
    input\="Plot the growth of solar energy generation globally and make some slides in HTML.",  
    environment="remote", \# This provisions the remote Linux sandbox hosted by Google  
)  
print(interaction.output\_text)

In that single call, the API provisions the sandbox, the agent researches the data via the web, writes the Python code to plot it, executes the code inside the Linux environment, and returns the final HTML.

But what if you want to build a custom agent tailored to your app's specific domain? Instead of writing hundreds of lines of complex orchestration code, you just define your agent using markdown files and register it with the API:

Python

\# Define your agent and register it as a managed agent  
agent = client.agents.create(  
    id\="data-analyst",  
    base\_agent="antigravity-preview-05-2026",  
    base\_environment={  
        "type": "remote",  
        "sources":  
    }  
)  

\# Now, just call your custom agent  
result = client.interactions.create(  
    agent="data-analyst",  
    input\="Analyze the Q1 revenue data and create a slide deck",  
)  
print(result.output\_text)

The Real Magic: The Interactions API

The sandboxing is great, but the Interactions API is the true hero here. It is explicitly designed to solve the state-management nightmare.

Previously, if a user asked a follow-up question, you had to append it to the entire conversation history and resend the whole massive payload back to the model. With the Interactions API, the core resource is the Interaction object, which acts as a permanent, server-side session record.

It logs the complete chronological sequence of what just happened: the model's internal thoughts, the function_call to the tools, the function_result, and the final model_output.

Because Google stores this environment and context securely on their end, the sandbox is persistent. Any Git repositories the agent downloaded, pip packages it installed, or files it generated remain entirely intact for the next turn. When the user asks a follow-up question, you don't resend the history. You simply pass the new prompt and link it using the previous_interaction_id.

My Verdict

The community has been begging for a first-class, provider-agnostic way to handle server-side context and infrastructure abstraction. We are tired of writing boilerplate orchestration code just to get an LLM to reliably trigger a Python script.

While the general tech media is busy talking about AI smart glasses and the "vibe coding" capabilities of the new Antigravity 2.0 desktop app, backend and full-stack developers need to be paying attention to the Interactions API. By entirely removing the friction of infrastructure setup, Google isn't just giving us a new tool; they are actively transitioning us from being syntax-typists into high-level system architects. We finally get to focus on what our agents do, rather than how they run.

And that is infinitely more exciting than Charli XCX playing from a pair of sunglasses.

Beyond Walls of Text: A Developer’s First Look at A2UI and Generative UI

Shubham Verma — Thu, 30 Apr 2026 19:32:56 +0000

At Google Cloud NEXT '26, the keynote slides were packed with the usual enterprise heavyweights: next-gen TPUs, cross-cloud data lakes, and the inevitable "agentic era" buzzwords. But while the infrastructure crowd was debating compute specs, one announcement actually made me rethink how I’ll be building apps moving forward: A2UI (Agent-to-User Interface).

During the Developer Keynote, Casey West and Ivan Nardini demoed how AI agents no longer have to live inside chat windows or spit out endless paragraphs of text. With A2UI, an open standard, agents can dynamically render actual UI components tailored to the user’s immediate context. It’s a quiet but massive shift: instead of building static screens that fetch data, we’re curating component catalogs and letting the AI orchestrate them on the fly.

I’m equal parts excited and skeptical, so I skipped the marketing docs and pulled down the official demo repo: VGVentures/genui_life_goal_simulator. It’s a multi-platform Flutter app built to showcase Firebase AI and Generative UI. After spending a few hours with the codebase, here’s what stood out, what works brilliantly, and what keeps me up at night.

Getting Started (Without the Boilerplate)

Setup was surprisingly clean. One Flutter codebase covers Android, iOS, Web, and macOS. You just run the flutterfire CLI to generate firebase_options.dart, enable Firebase AI and App Check in your console, and you’re good to go. No native config files, no platform-specific headaches. It’s the kind of frictionless setup that actually encourages experimentation.

The Architecture That Keeps AI in Check

The real win in this repo isn’t the AI itself, it’s how deliberately the architecture contains it. Generative UI can quickly become a maintenance nightmare if LLM logic bleeds into your presentation layer, but this project draws a hard boundary:

Repository Layer (SimulatorRepository): This is where GenUI lives. It owns the widget catalog, manages the SurfaceController, and handles communication with FirebaseAIChatModel. The rest of the app only receives a clean stream of events.
Business Logic (SimulatorBloc): Manages conversation state and pagination. Noticeably, there are zero Firebase or GenUI imports here. The BLoC has no idea how the UI is being generated, which keeps it testable and predictable.
Presentation Layer: Just hosts and animates whatever the SurfaceHost throws at it. It never instantiates GenUI objects directly.

When a user interacts, the app sends a system prompt (packed with widget schemas and persona instructions) to Firebase AI. The LLM streams back JSON, an adapter parses it, and the SurfaceController materializes real Flutter widgets from a predefined ~24-item catalog. User inputs write to a reactive DataContext and feed back into the model. It’s elegant, but it’s also asking a lot of the stack.

The Reality Check: Fragility, Testing, and the Unsung Heroes

Let’s be honest: letting an LLM dictate UI state is powerful, but it’s inherently fragile. The true MVPs in this repo are dartantic_ai and dartantic_firebase_ai. They enforce strict structured output schemas, which isn’t optional here. If the AI hallucinates a missing parameter or returns malformed JSON, your client app crashes. Period.

There’s also the testing gap no one’s addressing yet. How do you write reliable integration tests for a UI that’s generated at runtime? Traditional snapshot or widget tests fall apart. Teams adopting this pattern will need to invest heavily in deterministic prompt engineering—likely leveraging the newly announced Vertex AI Prompt Optimizer to ensure LLM outputs consistently map to registered CatalogItems. You’ll also need robust fallback UIs and schema validation middleware before this hits production.

The Bottom Line

NEXT '26 made it clear: the era of slapping a chatbot onto a CRUD app is over. We’re moving toward agentic, context-aware interfaces that adapt in real time. The genui_life_goal_simulator isn’t just a demo—it’s a blueprint for how to build dynamic, AI-driven UIs without sacrificing clean architecture.

But to make A2UI production-ready, we’ll need better testing strategies, stricter schema enforcement, and a lot more discipline around prompt design. The technology is here. Now it’s on us to engineer it responsibly.