Google Intelligent Endpoints for Enterprise Security and AI

At the #googleenterprise summit yesterday, we announced a unified surface where enterprise customers can interact with and get the best of #googleai. These surfaces are ChromeOS, Chrome browser, Cameyo by Google, Android, and Beam and they show up everywhere enterprise users are. Moreover, these endpoints are designed to transition work seamlessly, so you can start work on your #pixel and continue on your #chromebook without missing a beat. Feel free to message me on LinkedIn if you want to see any of these solutions in action.

Super excited about this coming together. At Google we have been continuing to re-think and redefine a modern paradigm of intelligent endpoint and application management designed for today's times.

Google has unveiled a radical new laptop strategy that replaces the Chromebook concept with AI-first “Googlebooks”, devices where Gemini AI is embedded directly into the operating system and even the cursor itself becomes an intelligent assistant.

Google just gave AI agents a standard interface to the physical world. The new Google Home MCP server lets compatible agents inspect home structures, query live device state, read historical events, and execute device-control actions. That is a much more consequential boundary than letting an agent search documentation or query a database. A tool call can now change physical state. Google puts protections around that boundary. Home MCP uses OAuth, applies rate limits, and blocks sensitive actions such as unlocking doors. Google also explicitly warns that connected agents can still produce unexpected or undesired behaviour. That warning is the important engineering detail. MCP standardizes how the agent discovers and calls tools. It does not decide whether an action should be allowed in a particular context. Once tools have physical consequences, I would want the application boundary to distinguish between at least: read device state propose an action execute a reversible action execute a consequential action Those should not automatically share the same permission model. An agent deciding that the outside lights should be turned off is one thing. Giving the same execution path authority over locks, alarms, cameras, or other sensitive systems is a very different risk. The broader lesson applies beyond smart homes. As agents move from retrieving information to changing real systems, tool design becomes authorization design. The protocol can make tools interoperable. It cannot decide how much authority an agent should have. Further reading: https://devkofi.com/blog Where are you putting approval boundaries today: inside the agent, inside each tool, or in a separate authorization layer? #AIEngineering #MCP #SoftwareArchitecture #AISecurity #AIAgents

One of our engineers ran Miito fully offline on a Samsung Fold this weekend. Not a mobile wrapper hitting our cloud. The model, the tools, and the agent loop on the device. Andres booted Linux on Android, forked Edge Gallery into an OpenAI-compatible server, put Gemma e4b on it, and got tool calls working. On a phone. With no outside inference. That is the product thesis in one object. Most enterprises do not have a model problem. They have a boundary problem. Security will not let the work leave the network. Compliance will not bless a third-party API. So the "AI initiative" stalls as a chatbot pilot that never touches real systems. Sovereignty mode is how we break that stall. Local models. Tool calling inside your perimeter. Agents that write and run where your data already lives, whether that is a phone, an air-gapped laptop, or a classified enclave. If your org has been waiting for agentic AI that does not require a trust leap to someone else's cloud, this is what we build. Happy to show you what it looks like inside your boundary.

Google Home has added MCP support, allowing AI agents to review camera activity, control devices, and build dashboards. Security risks associated with this feature persist.

A model scores 93% on the eval set. On a specific two-year-old Android chipset, after twenty minutes of continuous use, it starts producing garbled output. Same weights. Same eval score. Different hardware, different thermal state, different result. That gap is the norm for on-device and edge AI features, not an edge case. Teams building on phones, browsers, and embedded hardware keep reusing the eval harness they built for a hosted LLM API — a fixed prompt set, scored once, on one device, sitting on a desk connected to power. That harness has no concept of quantization accuracy loss that varies by task, chipset fragmentation that changes which code path actually runs, thermal throttling that shows up only after sustained use, or a model file that arrives on a user's device corrupted or half-downloaded. None of this is exotic. Google built an entire product, AI Edge Portal, specifically because manually testing across the hundred-plus Android device profiles that matter is impractical for most teams. Independent research on sustained inference load shows phones losing 15 to 40 percent of peak throughput as they heat up, with different devices throttling in very different ways. I wrote up what a testing program actually needs to look like once a model leaves the data center: a capability-level view of quantization accuracy loss, a device-tier test matrix, sustained-load thermal testing, and the telemetry a team needs once the vendor's own monitoring and rollback safety net is gone. Full piece here: #AITesting #EdgeAI #OnDeviceAI #MobileEngineering #QualityAssurance #SoftwareTesting

Google has opened pre-orders for its US$899 Googlebook laptops, built around Gemini AI, marking a deeper push into AI-powered personal computing💻 The devices aim to integrate Google's artificial intelligence into everyday tasks, potentially reshaping how users work, learn and create. The launch highlights intensifying competition in the laptop market as technology companies race to make AI assistants more practical, accessible and central to digital experiences. Will AI-powered laptops justify their premium prices for everyday users?

The architectural implications of 'Running a personal AI agent on an old Android phone instead of a server (medium.com/fsaint)' are underdiscussed: changes how we design systems at the boundary. Running a personal AI agent on an old Android phone instead of a server (medium.com/fsaint) https://lnkd.in/g-WxteXY #AI #ArtificialIntelligence #MachineLearning #Tech #SoftwareEngineering

Google’s $899 AI laptop isn’t a gimmick; it’s a test of how far hardware‑software bundling can go. The new “Googlebook” ships with a custom Tensor chip, a Chrome‑OS‑derived UI, and a built‑in Gemini assistant. It’s marketed as a productivity booster, but the price point puts it squarely in the premium notebook arena where you already have MacBook Pros and high‑end Dell XPS models. What makes it different is the tight integration with Google’s AI services. The laptop can stream Gemini responses locally for quick drafts, and it offloads heavy inference to the cloud when you need more horsepower. (I tried the demo on a borrowed unit and the latency was noticeable on the first query.) The trade‑off is clear: you get convenience at the cost of vendor lock‑in. The device relies on continuous internet access for the best experience, and the AI features drain battery faster than a vanilla Chrome OS machine. If you’re curious whether the AI edge justifies the premium, spend a week using the laptop for a single project and compare your total time‑to‑completion against a standard Windows laptop. The concrete takeaway: measure actual workflow time, not just feature hype. When you’ve used an AI‑enhanced laptop, how did the on‑device versus cloud processing affect your day‑to‑day workflow?