Google announced February 25 that Gemini can now autonomously execute multi-step tasks across Android apps—ordering Uber rides, placing Doordash deliveries, and chaining actions across multiple apps without user intervention. This isn’t conversational AI answering questions. This is AI that takes action. The feature launches March 11 on Samsung Galaxy S26 and rolls out to Pixel 10 in March, marking Google’s entry into “agentic AI”—the industry’s biggest shift from chatbots to autonomous agents.
What “Agentic AI” Actually Means
Unlike traditional voice assistants that respond to commands with information or single actions, agentic AI operates through continuous perception-reasoning-action loops. Gemini analyzes context, plans multiple steps, executes tasks across apps, and refines based on results—all autonomously. Tell it “order me lunch near my 2pm meeting,” and it checks your calendar for meeting location, searches nearby restaurants on Doordash, places the order for 1:45pm pickup, and asks for payment confirmation.
This is the entire AI industry’s current pivot. Gartner predicts 40% of enterprise applications will embed AI agents by end of 2026, up from less than 5% in 2025. The agentic AI market is projected to surge from $7.8 billion today to $52 billion by 2030. Google’s deployment makes this the first mainstream consumer agentic AI, putting agents in millions of Android users’ hands.
How Gemini Automation Works: AppFunctions Explained
Google built AppFunctions—an on-device API that lets Android apps expose data and functionality to AI agents. It’s Google’s version of the Model Context Protocol (MCP) used by Anthropic and OpenAI for cloud agents, but running locally on your phone instead of remote servers. Apps use the AppFunctions Jetpack library to create “self-describing functions” that Gemini can discover and execute via natural language.
From Google’s Android Developers Blog: “AppFunctions offers a platform API and a dedicated Jetpack library where developers can expose application-specific data and functionality for direct AI calls.” Gemini runs automation in a “secure virtual window” isolated from the rest of your device—it can access supported apps like Uber and Doordash, but not your full phone.
Moreover, on-device execution means privacy is preserved. No data flows to Google servers for automation tasks. The MCP comparison signals this is a serious developer framework, not a parlor trick. Developers already familiar with MCP patterns can apply the same concepts to Android.
The Privacy Tradeoff: What AI Sees (and What It Can’t)
Gemini operates in an isolated sandbox with explicit permissions. You must grant access before automation runs, and for financial transactions—ordering food, booking rides—Gemini prompts you to tap the final “buy” button. The AI doesn’t make purchases without human approval. According to TechCrunch, “you can watch progress in real time and stop the task if it’s making a mistake or getting stuck.”
However, the AI still sees data within supported apps—order history, location, payment methods. This is the fundamental tradeoff: AI needs app access to be useful. Google’s approach addresses most privacy concerns (on-device execution, isolated sandbox, human-in-the-loop for money), but doesn’t eliminate the core question: do you trust AI with access to your apps?
The answer determines whether agentic AI becomes mainstream or remains a flagship-only curiosity.
The Flagship-Only Problem
Gemini automation is available only on Pixel 10, Pixel 10 Pro, and Samsung Galaxy S26 series at launch. Mid-range and budget Android devices are excluded, despite Android’s massive install base on affordable hardware. Geographic restriction compounds the issue: US and Korea only initially.
Pixel 10 starts at $799, Galaxy S26 at $899. Combined install base by Q2 2026: estimated 10-15 million devices—a tiny fraction of Android’s 3+ billion active devices worldwide. This creates a digital divide where AI innovation is reserved for users who can afford flagship phones. It’s the opposite of Android’s “AI for everyone” marketing.
Furthermore, Android 17 (later in 2026) will expand capabilities to more manufacturers, but device requirements remain flagship-tier. Budget Android users, who represent the majority of the ecosystem, are locked out of Google’s marquee AI features.
Google Won the Mobile AI Race (By Powering Both Platforms)
In January 2026, Apple announced a $1 billion annual deal with Google to power Siri with Gemini’s 1.2 trillion parameter model, replacing Siri’s current 150 billion parameter architecture. This partnership boosts Siri’s success rate from 58% to 92% and response time to under 0.5 seconds—twice as fast as current Siri. Apple maintains that Gemini is used for training, not direct execution, preserving Apple’s on-device and Private Cloud Compute privacy model.
Consequently, Google’s Android automation announcement comes one month after locking in Apple as a paying customer. Gemini now powers AI on both Android and iOS. Apple’s $1 billion annual deal validates Gemini’s capabilities and makes Google the de facto winner of the mobile AI race—not by building the best phone OS, but by becoming the AI infrastructure layer beneath both platforms.
This isn’t just an Android feature. It’s Google capturing the entire mobile AI market.
What Happens Next
Gemini automation launches March 11 with the Galaxy S26 and rolls out to Pixel 10 series in March. US and Korea only at launch, with Android 17 expanding device support and geographies later in 2026. Supported apps at launch: Uber, Doordash, Grubhub, with Calendar, Notes, and Tasks already working on some devices.
The bigger question is adoption. Will users trust AI to spend their money? Will developers integrate AppFunctions, or is this another Google API that quietly fades? And will Apple respond with native iOS agentic features, or lean harder into the Gemini partnership?
March 11 will reveal whether agentic AI is the future of mobile computing or just flagship phone marketing.
