Google’s new AI agent can draft your emails, monitor your inbox and eventually spend your money

Google on Tuesday unveiled Gemini Spark, a personal AI agent designed to work around the clock — drafting emails, assembling documents, monitoring inboxes, and eventually making purchases — even when a user's laptop is closed and their phone is locked.

The announcement, made at Google I/O 2026, is the company's most ambitious attempt yet to transform its AI assistant from a tool that answers questions into one that autonomously completes tasks. It also arrives at a moment of extraordinary competition, as Microsoft, OpenAI, Anthropic, and Apple all race to build AI systems that don't merely converse but act — completing multi-step workflows with decreasing human supervision.

"We are in that part of the cycle where people want to see real value in the products they use on a day-to-day basis," Sundar Pichai, CEO of Google and Alphabet, said during a press briefing ahead of the keynote address. With Spark, he argued, that value comes from an agent that never stops working. It operates around the clock in Google's cloud, he said, so "you don't need to keep your laptop open to make sure it's running."

The product arrives at an inflection point for the technology industry, as Google, Microsoft, OpenAI, Anthropic, and Apple all race to build AI systems that don't merely converse but do — completing multi-step workflows with decreasing human supervision. It also raises urgent questions about trust, spending guardrails, and what happens when an artificial intelligence agent misinterprets a user's intent.

Spark will begin rolling out this week to a small group of trusted testers, with a beta planned for Google AI Ultra subscribers in the United States next week.

Inside the cloud architecture that lets Gemini Spark work while you sleep

Unlike conventional AI assistants that activate only when prompted, Gemini Spark is architecturally different. It runs persistently on Google Cloud infrastructure, powered by the company's new Gemini 3.5 Flash model and what Google calls the Antigravity agent harness — the same underlying system that powers the company's internal developer tools.

In practical terms, this means Spark can accept a complex instruction — "email my boss a status update pulling the latest figures from our shared spreadsheet and the project timeline in our Slides deck" — and then execute it across multiple Google applications without further input. The agent can pull context from emails, documents, and calendar entries, synthesize the information, and produce a finished output.

Josh Woodward, VP of Google Labs, Gemini App, and AI Studio, described the experience in visceral terms during the briefing: "When you use it, it almost feels like you're tossing things over your shoulder — Spark's catching them and gets the job done."

The cloud-based architecture is a deliberate design choice. Because Spark operates on remote servers rather than on a user's device, it can continue working through tasks after a user walks away. A student could ask Spark to build a study guide that updates itself as new assignments arrive from a professor. A small business owner could instruct it to monitor their inbox and flag potential customer inquiries. A parent could delegate the logistics of a neighborhood block party — tracking RSVPs, coordinating contributions, scouting venues. These are not hypothetical scenarios. Woodward said they reflect how early testers have actually been using the product.

Over the coming months, Google plans to expand Spark's capabilities significantly. The company will roll out MCP (Model Context Protocol) connections to more than 30 third-party partners, including Canva, OpenTable, and Instacart. Users will also be able to text and email Spark directly, create custom sub-agents for specialized tasks, and connect Spark to Chrome for web-based actions. Later this year, a new Android interface called Android Halo will provide live, at-a-glance visibility into what Spark is working on, displayed at the top of a user's phone screen.

Google compares its AI spending safeguards to giving a teenager their first debit card

For all its ambition, Spark confronts a fundamental challenge that has bedeviled every AI agent to date: How do you trust an autonomous system to act on your behalf — particularly when money is involved?

Google is acutely aware of the concern. When asked during the press briefing how Spark would avoid making unauthorized purchases, Woodward reached for an analogy that was striking in its candor. "On the team, we think a lot of it is like if you're giving a teenager their first debit card — there's sort of limits and sort of constraints around it, and that's how we'll be designing Spark as we go through the year," he said.

At launch, Spark will not autonomously make purchases. Users will be given explicit opportunities to review and approve any transaction before it goes through. But Google has built the infrastructure for a more autonomous future. Vidhya Srinivasan, who leads Google's ads and commerce teams, introduced the Agent Payments Protocol, or AP2 — a system designed to let AI agents make secure purchases within user-defined boundaries.

The concept works like this: a user tells their agent the specific brands, products, and spending limits they're comfortable with. If the criteria are met, the agent can automatically complete a purchase. AP2 creates what Google describes as a transparent, verifiable link between the user, the merchant, and payment processors, using privacy-preserving technology and tamper-proof digital mandates to ensure the agent is acting within its authorization. AP2 also generates a permanent digital paper trail, so that if a return is needed, the user and the merchant are looking at the same record. Google plans to bring AP2 to its products in the coming months, starting with Gemini Spark.

The system is underpinned by the Universal Commerce Protocol (UCP), an open-source standard Google announced earlier this year that gives agents and commerce systems a common language across the entire shopping journey. The UCP Tech Council now includes Amazon, Meta, Microsoft, Salesforce, and Stripe — a remarkable coalition that underscores how seriously the industry takes the prospect of agent-driven commerce.

Google also announced the Universal Cart, an intelligent shopping cart that works across merchants and Google services. Users can add items while browsing Search, chatting with Gemini, watching YouTube, or reading Gmail. The cart then works in the background — tracking price drops, surfacing deals based on payment card perks, and even flagging product incompatibilities. The shopping infrastructure is rolling out in the U.S. this summer across Search and the Gemini app, with YouTube and Gmail to follow.

How Google, OpenAI, Microsoft, Anthropic, and Apple are racing to build the definitive AI agent

The announcement lands in the middle of the most intense competitive period in AI history. Google, Microsoft, OpenAI, Anthropic, and Apple are all racing to ship autonomous agents that can do real work — and each is placing a fundamentally different architectural bet on how to get there.

OpenAI recently unified its Operator and deep research capabilities into ChatGPT agent — a system that brings together website interaction, information synthesis, and conversational intelligence. It carries out tasks using its own virtual computer, shifting between reasoning and action to handle complex workflows. The company emphasizes that users remain in control, with ChatGPT requesting permission before taking consequential actions. But the product has faced scrutiny over reliability. OpenAI's Computer-Using Agent scores 38.1% on OSWorld, the industry benchmark for computer use tasks, while humans score over 72%.

Anthropic launched its Claude Computer Use Agent in research preview in March, giving Claude the ability to see, navigate, and control a user's desktop — clicking buttons, opening applications, filling spreadsheets, and completing multi-step workflows. Claude Cowork handles tasks autonomously — users give it a goal and Claude works on their computer, local files, and applications to return a finished deliverable. Anthropic has iterated aggressively, recently shipping ten pre-built financial agents and pursuing deep Microsoft 365 integration.

Microsoft introduced Copilot Cowork to move beyond chat and into execution — helping users delegate real tasks and have them completed. Cowork runs in the cloud, meaning users don't have to worry about closing their laptop. The system is grounded in Work IQ, Microsoft's intelligence layer that understands organizational data, tools, and structure. The shift moves Copilot from a sidebar helper to an orchestrator of autonomous agents.

Apple is also preparing a revamped Siri for WWDC 2026 that will act as an "always-on agent" capable of handling tasks across apps using personal data. Google's Gemini models will help power the upgraded Siri through a multi-year deal reportedly costing Apple around $1 billion per year.

The convergence is unmistakable: every major platform is moving from assistants that talk to agents that act. But each is approaching the problem differently. OpenAI's agent operates primarily through a browser. Anthropic's works directly on a user's desktop. Microsoft's is tightly bound to the Office 365 ecosystem. Apple's emphasizes on-device processing and privacy. Google's approach with Spark is distinctive in its bet on cloud persistence and deep integration with its own services. 

Rather than controlling a user's screen pixel by pixel, Spark works through structured integrations — Google's own Workspace APIs, and increasingly, third-party connections through MCP. The advantage is reliability and speed: structured tool use is far more predictable than screen-reading. The disadvantage is that Spark, at least initially, can only act within the systems it's been connected to.

The AI model behind Spark processes trillions of tokens a day — and Google says it could save enterprises billions

Spark's capabilities are inseparable from the model that drives it. Gemini 3.5 Flash, also announced Monday, is Google's new workhorse AI model — designed specifically for the demands of agentic workflows.

The performance claims are important. Google says 3.5 Flash outperforms its previous frontier model, Gemini 3.1 Pro, across nearly all benchmarks, while running four times faster than comparable frontier models in terms of output tokens per second. An even more optimized version, available within Google's Antigravity development platform, runs twelve times faster.

Pichai framed the economics bluntly. Companies processing roughly one trillion tokens per day on Google Cloud — a figure he said top enterprise customers are hitting — could save over $1 billion annually by shifting 80% of their workloads to a mix of Flash and frontier models like 3.5 Pro. In a market where, as Pichai noted, CIOs are already "blowing through their annual token budgets and it's only May," the cost argument may matter as much as the capability argument.

Internally, Google's own developers have been consuming Gemini 3.5 Flash at a staggering and rapidly accelerating pace. In March, Google was processing about half a trillion tokens per day internally. That figure has since grown to more than three trillion — doubling roughly every few weeks. Pichai described this as a "powerful feedback loop" that continually improves the model.

Koray Kavukcuoglu, CTO of Google DeepMind and Chief AI Architect for Google, said the model's speed is what makes agentic use cases practical. "3.5 Flash is especially good when deploying multiple agents simultaneously and completing long-running tasks," he said during the briefing, adding that Google had successfully tested agents building "a working operating system entirely from scratch."

The 3.5 Pro model, the more powerful sibling, is currently being tested internally and will roll out next month.

What Gemini Spark costs and where it fits in Google's new subscription tiers

Gemini Spark will be available to Google AI Ultra subscribers. The company is simultaneously restructuring its subscription tiers to make the technology more accessible. A new Ultra plan at $100 per month provides a 5x higher usage limit than the Pro plan, along with priority access to Antigravity and 20TB of cloud storage. The top-tier Ultra plan drops from $250 to $200 per month, with a 20x higher usage limit and access to the full suite of capabilities.

Both tiers include Gemini Spark, the Daily Brief agent — a proactive morning digest that triages email, calendar, and tasks overnight — and access to the new Gemini Omni and 3.5 Flash models. The pricing positions Spark as a premium product — more expensive than Anthropic's Claude Pro at $20 per month, but comparable to the higher tiers of competing products like Claude Max ($100–$200/month) and OpenAI's ChatGPT Pro ($200/month).

Why privacy, reliability, and ecosystem lock-in could undermine Google's agent ambitions

The risks are real and multidimensional.

Reliability remains the industry's greatest challenge. Even the best AI models hallucinate, misinterpret instructions, and make errors that a human would never make. An agent that drafts an email to the wrong person, misreads a spreadsheet figure, or sends a payment to the wrong merchant could create consequences that are difficult to reverse. Google's approach of requiring explicit approval for high-stakes actions like spending money or sending emails is a sensible safeguard — but it also limits how autonomous the agent can actually be. An agent that asks for confirmation at every turn isn't much of an agent at all.

Privacy is another concern. Spark's ability to synthesize information across a user's entire Gmail inbox, calendar, documents, and chat history means it has an extraordinarily deep view of a person's digital life. Google says Spark operates on a fully managed, secure runtime with isolated ephemeral virtual machines, encrypted credentials, and Data Loss Prevention policies. But the concentration of personal context in a single AI system — accessible through natural language — creates a surface area that will attract scrutiny from regulators, privacy advocates, and security researchers.

Market timing is uncertain, too. The consumer appetite for always-on AI agents is unproven at scale. Google says the Gemini app has 900 million monthly users, but it's unclear how many of those users are ready for the conceptual leap from "ask a question, get an answer" to "delegate a task, trust the outcome." The history of digital assistants — from Clippy to early Siri to Alexa — is littered with products that promised proactive intelligence and delivered frustration.

And then there is the question of ecosystem lock-in. Spark works best within Google's own services. While MCP connections to third-party apps will broaden its reach, the initial experience is one of deep Workspace integration. For the billions of people who live inside Google's ecosystem, this is a natural fit. For those who split their digital lives across Microsoft, Apple, and other platforms, Spark's utility will be more limited — at least initially.

Woodward acknowledged as much when asked whether Spark would remain confined to the Google ecosystem. "It's going to be cross-platform in two ways," he said — through MCP integrations with third-party apps, and through availability on the web, Android, and iOS, with tasks syncing across devices via the cloud.

The real test for Gemini Spark isn't whether it can do the work — it's whether people will let it

Google's bet with Gemini Spark is that the AI industry's center of gravity is shifting from models that think to systems that act — and that the company best positioned to win that transition is the one with the most comprehensive set of consumer services to act within. It is a bet backed by enormous infrastructure investment. Google expects to spend approximately $180 to $190 billion in capital expenditure this year — roughly six times what it spent in 2022 — much of it on the AI compute required to run agents like Spark at scale for hundreds of millions of users.

The technology, in other words, is arriving. The models are fast enough, the integrations deep enough, the payment rails secure enough. Google has built a system that can draft your emails, organize your calendar, monitor your inbox, and soon enough, spend your money — all while you sleep.

But the hardest problem in artificial intelligence has never been making a machine capable. It has been making a human comfortable. For two decades, Google's core promise has been ten blue links and a search box — a transaction built on the assumption that the user is in control. Gemini Spark asks users to renegotiate that relationship entirely, to hand a set of keys to a system that is brilliant, tireless, and still, by its maker's own admission, best compared to a teenager with a debit card.

Gemini Spark rolls out to trusted testers this week, with a broader beta for U.S. Google AI Ultra subscribers expected next week.