AI agents need three things to do real work: a real runtime, live app context, and governance that ships with them. Here is how CloudTop delivers each one — today.
One artifact, two faces: humans see the interface; AI sees structured tools and live state through an on-device MCP server — under the same permissions as its user.
Most AI reaches your tools from outside — an API or server, data in transit. CloudTop runs the connection on the client, inside your running apps. Nothing leaves.
Six questions any organization should ask before letting AI act — and what CloudTop ships for each one.
Allow-or-deny is too crude for agents that chain actions. Permissions must be expressible, revocable, and auditable — for people and AI.
Every app is a declarative document, validated before execution. ACLs scope each person and each AI to app data, shared knowledge, prompts, and documents.
Agents that act need isolation — somewhere mistakes and manipulation can't reach the rest of your world.
Agents run in a sandboxed runtime on your device and act through each app's typed MCP tools — never raw OS control, no rented cloud desktop.
Prompt injection now turns into real actions. Controls have to arrive together with the agents — not years after the first wave of abuse.
The AI authenticates as its user; tokens never leave the device. Schema validation and row-level ACLs cap what any injected prompt can do.
If AI lives in browsers, phones, and SaaS, an agent platform tied to a single OS is a bet against where work is going.
The same app runs native on Windows, macOS, Linux, mobile, and web via the marketplace — with data under European jurisdiction.
Builders need to create, supervise, and trust agent-era software — without being locked into one vendor's stack.
Generate the front end with any AI tool and bind it to the database you already run. Apps arrive AI-native; agents and tools extend them.
Vendor team plans solved sharing — and created lock-in. The team's work doesn't follow the model: switch LLMs, and the context stays behind.
Apps are multi-user by default. Teams share files and AI memory as portable artifacts while each member picks their model. One memory. Any model.
Create the front end with whatever AI builder you like — no lock-in to one tool.
Point it at the database you already run — no backend rebuild.
Instantly AI-native — connected to your models and apps. Credentials stay on the device.
Publish to the marketplace — native on Windows, Mac, Linux, and mobile.
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