The strategic question is no longer whether AI assistants will answer questions. It is whether they will become the place where users design, shop, book, create and manage services. Google’s new Connected Apps Platform points toward that future by giving businesses a way to make their applications available inside Gemini and Search.
Google announced the Connected Apps Platform as a preview for developers. The company says the platform is designed to let developers build and manage integrations that work across its AI products, rather than requiring users to open each service independently.
That changes the role of Search. For much of the web’s history, search engines have functioned as directories. They ranked links, sent users to external websites and left the final action to the user. A connected app can allow the AI system to move further into that process by invoking a service, using an authenticated account and helping complete a task within the conversation.
Google’s examples illustrate the difference. A user can ask Canva to help design a flyer or ask YouTube Music to create a playlist, according to the company’s connected-app materials. TechCrunch reported that more than 50 connected apps are already available in Gemini, including integrations from Adobe, Experian, Localiza and Peloton, as well as Google services such as Workspace, Photos, YouTube Music and Google Home.
Google says those existing Gemini integrations will begin appearing in Search in the following weeks. Calm is also planning to launch on both services, according to the report. The result would be a more consistent access point for applications, with Gemini and Search acting as the interface through which users discover and operate them.
A technical bridge into the agentic web
The platform is built around the Model Context Protocol, or MCP, a framework that allows an AI system to interact with external tools and services. Google’s Connected Apps Platform documentation describes two integration paths: a cloud-hosted MCP server and Android MCP, used with Google’s AppFunctions APIs.
The distinction matters because Google is not asking every developer to rebuild an application from scratch. Instead, developers can expose selected capabilities to Google’s AI products through an interface designed for tool use. A service might provide an agent with the ability to search a catalog, create a document, check availability or perform another action that the developer has chosen to support.
The platform also includes an authorization layer. Google’s documentation describes OAuth support and access to users’ data and actions across Gemini and Search. That means connected apps are not simply content feeds. They can become operational interfaces, provided the user grants the necessary permissions and the application meets Google’s requirements.
Developers register and manage integrations through the Connected Console. They can choose whether an app appears in Gemini, Search or both, then monitor traffic, latency and error rates. Those measurements could become important as businesses evaluate whether AI referrals lead to useful actions rather than superficial exposure.
The platform also gives developers some control over how their services appear. Google says businesses can retain elements of their brand identity, including verified logos and authenticated accounts. That may help reduce confusion in an environment where users interact with an AI intermediary instead of visiting a company’s familiar website or app.
A new form of optimization
The commercial consequences could resemble search engine optimization, but with a more operational focus. Traditional SEO rewards pages that are discoverable, relevant and authoritative in search results. Connected apps introduce another set of questions: Can an AI agent identify the service correctly? Can it authenticate the user? Does the integration respond quickly? Can it complete a task without errors or unnecessary handoffs?
This creates a potential advantage for companies that invest early in reliable integrations. A travel company, for example, may no longer compete only to rank for destination searches. It may also compete to become the service an assistant selects when a user asks to compare routes or make a reservation. A fitness platform may need to ensure that an agent can understand memberships, retrieve account information and schedule a class accurately.
The distribution benefit is attractive, but it comes with dependency. If Gemini and Search become the main gateways to third-party services, Google could gain influence over which applications are surfaced, how users interact with them and how much information businesses receive about those interactions. Developers would gain access to a large audience, but they would also be operating within Google’s technical, commercial and policy framework.
Google’s Connected Apps Platform policy says connected apps must be reliable, transparent and safe. It also gives Google the ability to reject, suspend or remove applications for quality, security, privacy or policy violations. Those safeguards are necessary when an AI system can take actions on a user’s behalf, but they reinforce the platform’s role as a gatekeeper.
The limits of the preview
For now, the public beta is limited to developers in the United States. Google has not announced when the preview will become broadly available. That leaves companies with an immediate strategic decision: whether to spend engineering and compliance resources on an integration before the market has established clear standards for traffic, attribution and revenue.
The early test will not simply be how many apps join the platform. It will be whether users trust assistants to operate those services, whether businesses see meaningful transactions and whether developers can measure the value of being mediated by Google.
If those conditions are met, connected apps could turn Search and Gemini from places that recommend software into places that operate it. That would make Google more than a discovery layer. It would make the company a central broker between users and the applications they rely on, a position that could define the next phase of the web.
Alex Carter is not a person. No notebook, no deadlines, no face behind the name — just a byline this newsroom publishes under. Here is the production line underneath it, because a name beside a portrait reads like a journalist, and this one is not one.
The models. Writing: gpt-5.6-luna and qwen3-max. Out on the live web: gpt-5.6-luna and gpt-5.6-terra. Pictures: gpt-image-1 and gpt-image-1-mini. Swap one in the newsroom and this line swaps with it — it is read off the machines, not typed here.
How a story is made
- Research. The searching model reads around the story, pointed at primary sources — the filing, the post, the repository — rather than at somebody else's write-up of them.
- Writing. The writing model drafts it against what was found, at Alex Carter's usual length and in Alex Carter's usual register.
- The loop. A reviewer reads the draft and sends it back with notes. Then reads it again. A piece can go round several times before it leaves the building.
- Enrichment. A quotation has to appear word for word on the page it is taken from. A chart may only use figures that appear in the source it cites. Whatever fails is dropped, and the reason is kept.
- Fact check. A last pass hunts for claims the article makes and its sources do not.
- A human stop. Sensitive subjects are held for a person to read before publication, and a person can kill any of it at any point.
If that sounds less like a newsroom and more like a factory: quite. It is called Press Factory.
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