Google's Gemini Spark: What Its New 24/7 AI Agent Actually Does (and Whether You Need It)

What Gemini Spark actually is

Let’s start with the plain version, because the keynote made it sound like science fiction. Gemini Spark is a personal AI agent that lives inside the Gemini app. The difference between Spark and the chatbot you might already use is one word: persistence.

A normal chatbot answers you and then forgets the task the moment you close the tab. Spark doesn’t. It runs on Google’s own cloud servers, so it keeps working on a job even when your laptop is shut and your phone is in your pocket. [1] You hand it something like “research three event venues in Manchester under 2,000 pounds and put the options in a doc,” and it goes off and does the legwork while you get on with your day.

If the phrase “AI agent” still feels fuzzy, you’re not alone. The short definition: an agent is AI that can take actions across multiple steps and tools to finish a goal, not just produce one reply. We broke this down properly in our plain-English guide to AI agents, and Spark is one of the first versions aimed squarely at non-technical people rather than developers.

Spark was announced at Google I/O on 19 May 2026, alongside a new model called Gemini 3.5 Flash and a major rework of Google Search. [1,2] It’s available as part of Google AI Ultra, the company’s top-tier paid plan. That last detail matters, and we’ll come back to it.

How it differs from the Gemini you already use

Most people’s experience of Gemini so far has been a chat box. You type, it replies, the end. Spark changes three things about that relationship.

It connects to your stuff. Spark plugs directly into Gmail, Docs, Sheets, Slides, and Chrome. [1] So instead of you copying an email into a chat window, it can read the thread itself, draft the reply in your Docs, and drop a summary into a Sheet. The work happens where your work already lives.

It handles long jobs. Regular chatbots are sprinters. Spark is built for marathons: the kind of task that has six steps and takes twenty minutes of clicking around. Google calls these “long-horizon” tasks, which is just a fancy way of saying “the boring multi-step things you keep putting off.” [2]

It works in the background. Because it runs in the cloud rather than on your device, you can kick off a task and walk away. [1]

If you already use Claude’s Projects feature to keep context in one place, this will feel familiar. The difference is that Spark acts on that context rather than just remembering it. Our guide to Claude Projects is a good primer if you want to understand the “persistent memory” idea first.

What it can do for your actual work

Enough theory. Here is what this looks like for someone in a normal job.

If you’re in operations or admin: “Go through my inbox from the last week, pull out every message that needs a decision from me, and list them with a one-line summary and a suggested reply.” That’s the kind of triage that used to eat your first hour. (We have a whole inbox triage workflow if that’s your daily pain.)

If you’re in marketing or comms: “Research what our three main competitors posted on LinkedIn this month, group the themes, and draft a one-page summary in a Doc.” Spark can browse, collect, and write the draft in one run.

If you’re a consultant or founder: “Monitor these five companies for any funding news this week and email me a digest on Friday.” This is the genuinely new part. It’s a task that repeats, runs on its own, and reports back.

Notice the pattern. None of these are “write me a poem” requests. They’re errands. Spark is most useful for the work that’s tedious rather than hard, which is exactly the work most professionals have too much of.

The quieter announcement: agents inside Search

Spark got the headlines, but the change that will touch more people is what Google did to Search itself. The search bar now expands to handle longer, more conversational questions, and it runs on the new Gemini 3.5 Flash model by default. [3]

More interesting: you can now create small “agents” inside Search that track or research a topic over time. [3] Apartment hunting, watching for a product restock, monitoring a price. The search engine does the checking for you and pings you when something changes.

Why does this matter for your work even if you never pay for Spark? Because it confirms where Google is heading. Search is shifting from “answer my question right now” to “keep an eye on this for me.” [5] If your business depends on being found through search, that shift is worth watching closely. It’s the same trend we covered in how AI answers are changing search behaviour.

The honest limitations

Here’s the part the keynote skipped. Spark is impressive, but let’s be honest about what it isn’t.

It’s behind a premium paywall. Spark is part of Google AI Ultra, the most expensive consumer tier. [1] For most professionals, that’s a real cost to justify when the free version of Gemini, ChatGPT, or Claude already handles the bulk of day-to-day work.

Agents still make mistakes, confidently. An agent that takes six steps has six chances to go wrong, and it won’t always tell you when it has. You have to check the output the same way you’d check an intern’s first draft. Handing it something irreversible (sending emails, making bookings) without review is asking for trouble.

It needs your Google data to be useful. The magic comes from the connections to Gmail and Docs. If your work lives in Microsoft 365 or somewhere else, a lot of that value evaporates. For Microsoft users, Copilot’s recent updates are the more relevant story.

Is it worth paying for? A simple test

You don’t need a spreadsheet to decide this. Ask yourself one question: do you have a repeating, multi-step task that you dread and keep postponing?

If yes, and that task touches Gmail, Docs, or the web, Spark might genuinely earn its place. Think weekly competitor digests, recurring research, inbox triage at scale.

If your AI use is mostly “help me write this email” or “explain this concept,” save your money. The free tier of any major chatbot does that just as well, and you’ll get more value from learning to prompt it properly than from paying for an agent you’ll use twice.

A reasonable middle path: most teams are still early. Only around one in five marketers, for example, are using AI agents to run tasks end-to-end so far. [6] You are not behind if you wait a quarter and let the rough edges get sanded down.

What to do this week

You don’t need AI Ultra to act on any of this. Here’s the practical move.

First, open the Gemini app or whatever chatbot you use and try one “agent-style” task by hand: “Summarise these three articles into five bullet points and suggest what I should do next.” Notice how you brief it. That skill transfers to every agent product coming this year.

Second, make a short list of the repeating tasks you dread. That list is your real shopping guide. When agent tools get cheaper (and they will), you’ll know exactly what to point them at instead of being dazzled by a demo.

Third, if search traffic matters to your business, start paying attention to how your brand shows up inside AI answers, not just blue links. That’s the shift Spark and the new Search are both pointing at, and it’s the one most likely to affect your bottom line.

The agents are coming whether or not you buy this particular one. The professionals who win aren’t the ones with the most expensive subscription. They’re the ones who already know which jobs to delegate.