How to build next‑gen AI apps with Google AI Studio and Antigravity

AI development is changing fast. Instead of stitching together tools and writing every line of code by hand, you can now go from idea to working product with a stack of AI-native tools. Google is pushing hard in this direction with two key products: Google AI Studio and Google Antigravity.

Together, they’re becoming a full pipeline for exploring ideas, building apps, and running powerful AI agents that can work across your code, data, and cloud infrastructure. Here’s how the new capabilities fit together—and how you can actually use them.

What Google AI Studio is becoming

Google AI Studio started as a simple playground to test prompts and models. It’s now evolving into a one-stop hub where you can:

• Try the latest Gemini models in a playground
• Use ready-made agents for research, data analysis, customer support, and more
• Build full apps with a visual interface and system instructions
• Integrate with Google Workspace (Sheets, Drive, etc.)
• Deploy directly to Cloud Run with one click

Instead of treating AI as a bolt-on, AI Studio is designed as the fastest way to go from “prompt” to “app”—especially if you’re a startup, solo builder, or small team.

Using agents inside Google AI Studio

Agents are at the core of the new AI Studio experience. Rather than just answering a single prompt, an agent can follow instructions, plan, call tools, and return structured results.

Inside the playground, you can pick from different agent types, such as:

• Data Analyst – for analyzing datasets and generating insights
• Research Agent – for deep market or domain research
• Customer Support – for support workflows and FAQs
• Document Processor – for extracting and transforming information from documents

You choose an agent, define its role and constraints in the system instructions, and then give it a detailed task. AI Studio handles the rest, including long-running jobs with the Max preview model for overnight or heavy research tasks.

Example: validating a new business with AI Studio

To show what’s possible, imagine you want to open a pet shop and boarding service in the San Francisco Bay Area but have no background in the pet industry. In AI Studio, you can:

1. Open the Research Agent in the playground.
2. Give it a detailed persona (for example, “premier retail market research analyst specializing in the Bay Area and Silicon Valley”).
3. Ask for a full market analysis: target demographics, micro-markets, pet ownership correlations, competition, pricing, and inventory strategy.

The agent generates a structured research plan, runs through it, and returns a comprehensive report. You can inspect its “thoughts” and intermediate steps, approve or refine its plan, and then use the final report as the foundation for your business decisions.

If you just need a quick snapshot instead of a deep dive, you can adjust the system instructions and run a lighter analysis using a faster model.

Simulating user feedback with agentic focus groups

Once you have a basic website or product concept, you need feedback. AI Studio introduces an “agentic focus group” app that lets you simulate feedback from multiple user personas at once.

You can remix this app and define personas such as:

• Skeptical CFO
• UX Lead
• Busy Executive
• Status Seeker
• Any custom audience segment relevant to your product

Paste your website URL or content into the app, and the agents will:

• Describe their overall impression
• Run sentiment analysis
• Highlight friction points and usability issues
• Score your site across different dimensions (for example, clarity, trust, appeal)

This gives you a structured way to iterate on your product before you have real users. It’s especially useful for online businesses and early-stage startups trying to find a niche and refine messaging.

Designing better UIs with Stitch and AI Studio

If your first website or app looks too basic, you don’t have to start from scratch. Google’s Stitch tool can generate design concepts that you can then turn into working apps in AI Studio.

A typical workflow looks like this:

1. Open Stitch and ask it to “create a landing page for my pet shop in the Bay Area.”
2. Review the design suggestions Stitch generates.
3. Export a design you like directly into AI Studio.
4. In AI Studio’s Build tab, tell the model: “Build me an app with screenshots that look like this.”

The result is a new app structure that reflects the design direction from Stitch, which you can then customize further in AI Studio.

If you want a deeper dive into AI Studio’s no-code side, you may also find this guide on using Google AI Studio like a pro helpful.

Connecting AI Studio to Google Workspace

AI Studio is also gaining tighter integration with Google Workspace, which makes it much more useful for real operations—not just prototypes.

For example, you can:

• Use Google Sheets as a live backend for inventory, bookings, or CRM data.
• Store images and assets in Google Drive and reference them from your app.
• Keep your AI-powered dashboards and your spreadsheets in sync.

In the pet shop example, you might already have a simple inventory in Sheets with columns like pet name, type, breed, photo URL, and health status. In AI Studio’s Build tab, you can ask:

“Create a dashboard for my pet shop and allow integration with Google Sheets for inventory management.”

AI Studio generates a dashboard UI, connects it to your Sheet, and lets you:

• See total pets, available vs. adopted, and health status at a glance.
• Browse an asset gallery of pet photos.
• Update records in real time—changes in the app update the Sheet, and vice versa.

Building marketing and growth tools with AI Studio

After research, website, and operations, the last piece is growth. AI Studio can also generate a marketing and go-to-market dashboard for your business.

You can prompt it with something like:

“Create a marketing tool to help me with go-to-market for my pet shop. Give me 10 CTAs and help me optimize ads.”

The resulting dashboard might include:

• A growth score based on your current website and messaging
• Friction and conversion analysis
• SEO and metadata review
• Suggested campaigns and channels
• An ad simulator to test hooks, visuals, and copy

From there, you can plug into creative tools (like video or image generators) to produce actual assets—logos, brand identity, video ads, and more. If you’re interested in a similar end-to-end marketing workflow, you might also like this breakdown of building a brand with AI Marketing Studio.

What’s new in Google Antigravity 2.0

While AI Studio focuses on exploration and app building, Antigravity is Google’s agent-first environment for deeper development work. Originally launched as an AI-powered IDE, it has now evolved into Antigravity 2.0—a standalone agent platform designed for complex, ongoing tasks.

The key shift is moving from “an IDE with an agent” to “an agent-first workspace” where:

• Your main view is conversations with agents, not just files.
• Multiple agents can work in parallel across multiple projects.
• You can still use any IDE you like for coding, while Antigravity orchestrates the agents.

Core agent features in Antigravity 2.0

Antigravity 2.0 introduces several new primitives that make agents more capable and scalable.

Sub-agents for complex tasks

When you give an agent a big, vague task, it can now spin up sub-agents to handle specific subtasks. For example, if you ask it to summarize everything launched in Antigravity at I/O, the main agent can:

• Break the work into smaller, well-defined tasks.
• Launch sub-agents to handle each one in parallel.
• Collect their results and synthesize a final answer.

This keeps the main agent focused on orchestration and high-level reasoning, while sub-agents execute tightly scoped work.

Asynchronous background tasks

Many development tasks involve long-running commands—like installing packages, running builds, or executing tests. Antigravity can now run these as background tasks:

• The agent kicks off a command asynchronously.
• You see its status in a side panel.
• When it completes, the agent gets notified and continues its workflow.

This means the agent can write code while dependencies are installing, then immediately move to running or previewing the app once everything is ready.

Hooks for custom logic and policies

Hooks let you inject your own scripts into the agent’s lifecycle. Defined in JSON, they allow you to say things like:

• “Before running this tool, execute this script.”
• “Before ending a turn, run these validation checks.”

You can use hooks for custom security checks, style enforcement, or any company-specific rules you want the agent to follow automatically.

Artifacts: understanding what your agents are doing

As agents run longer and more complex workflows, reading raw conversation logs becomes overwhelming. Antigravity introduces artifacts as a clearer way to see what’s happening.

Artifacts can be:

• Markdown documents (for example, implementation plans, walkthroughs, task lists)
• Code diffs and file changes
• Visual assets like screenshots

You can open an artifact, review it, and leave inline comments. The agent then incorporates your feedback into its next steps. This gives you a higher-level, human-friendly view of progress and decisions, without losing the ability to drill down into details.

Projects and permissions: keeping agents under control

Antigravity 2.0 introduces a more flexible concept called projects. A project is a collection of resources and settings that agents can work with, including:

• Multiple folders or repositories (for example, a microservices system)
• Tools and Model Context Protocol (MCP) integrations
• Custom skills and system prompts
• Permissions and safety policies

Instead of tying one agent to one repo, you can now:

• Connect several folders to a single project so an agent can work across them.
• Define what files outside those folders it’s allowed to access.
• Configure which terminal commands are always allowed, always blocked, or require approval.

This helps avoid the “agent gone wild” problem. For critical codebases, you can keep permissions tight and require frequent approvals. For low-risk projects, you can relax constraints and let the agent move faster—as long as it avoids dangerous commands like destructive deletes.

Working faster with audio and slash commands

Antigravity also adds quality-of-life features that make it easier to work with agents day to day.

Live audio input. You can talk to the agent instead of typing. Behind the scenes, Google’s latest audio models clean up filler words and structure your speech into clear prompts, so the agent receives coherent instructions.

Slash commands. Several slash commands speed up common workflows:

• /schedule – Create a scheduled task in natural language (for example, “Every weekday at 9 AM, summarize open PRs in this project”).
• /goal – Tell the agent to pursue a goal autonomously without asking for intermediate feedback unless necessary.
• /browser – Instruct the agent to use a browser sub-agent (via Chrome DevTools MCP) to test and interact with web apps, capture screenshots, and more.
• /grill-me – Ask the agent to question you first and clarify requirements before it starts implementing, to avoid vague or misaligned work.

Scheduled tasks and always-on agents

With models like Gemini 3.5 Flash running extremely fast (hundreds of tokens per second in Antigravity’s optimized setup), it becomes practical to let agents run in the background on a schedule.

Scheduled tasks let you define recurring jobs such as:

• Daily engineering digests (open PRs, failing tests, recent deploys)
• Post-deploy health checks on new services
• Regular data quality checks or report generation

To set one up, you define:

• A task name
• The project (so it inherits the right tools and permissions)
• The schedule (for example, daily at 9 AM)
• The prompt (what you want the agent to do)

Each run appears as a conversation in the project, so you can open it, read the results, and continue the dialogue if you need follow-up analysis.

Beyond the UI: CLI, SDK, and managed agents

Not everyone wants to live in a GUI. Antigravity’s agent harness is also available through:

• A CLI, for terminal-first workflows.
• An SDK, so you can embed the same agent behavior into your own apps and services.
• A managed agent in Google’s Interactions API, for teams that want to integrate powerful agents without managing all the infrastructure.

This means you can prototype in AI Studio, refine and automate in Antigravity, and then ship production-ready agent capabilities into your own products using the same underlying stack.

Putting it all together

Google AI Studio and Antigravity 2.0 are converging into a powerful ecosystem:

• AI Studio helps you explore ideas, run research, build UIs, connect to Workspace, and deploy to Cloud Run.
• Antigravity gives you an agent-first environment to extend, maintain, and automate those apps across multiple projects and tools.

From validating a business idea to launching a live product and automating your daily workflows, you can now do most of the journey with this stack. The next step is simple: pick one real problem—whether it’s a dashboard, internal tool, or side project—and build the first version end-to-end using AI Studio and Antigravity. Then iterate with agents instead of starting from scratch each time.