Microsoft’s Project Solara concept shows how AI agents may reshape work devices, operating environments, and enterprise interfaces by moving from app-first workflows to agent-first experiences.
For years, digital work has been organized around apps. Users open an email app, a spreadsheet app, a browser, a CRM, a calendar, a project management tool, and a messaging platform. AI assistants have mostly been added on top of this app-based world.
Microsoft’s Project Solara direction suggests a different possibility: enterprise devices and interfaces built around AI agents first. In this model, the agent becomes the main layer that understands context, coordinates tasks, and calls the right software or interface only when needed.
For NexusAI users, this is important because it shows how AI may move beyond websites and chat windows. The next wave of workplace AI could live inside wearables, desk devices, browsers, operating environments, and cloud-connected work hubs that make software feel less fragmented.
From app-first work to agent-first work
The app-first model asks users to remember where everything lives. A support worker may need to move between a ticketing system, customer history, product documentation, internal chat, scheduling tools, and reporting dashboards. The user becomes the integration layer.
An agent-first model tries to reverse that burden. The user explains the goal, and the agent brings together the right context, tools, permissions, and interface. Instead of manually switching between systems, the worker interacts with a more adaptive digital layer.
Why devices matter in the AI agent race
Most AI product discussion focuses on software, but hardware and device context may become just as important. If an AI agent knows whether a user is at a desk, in a store, on a warehouse floor, in a meeting, or visiting a customer site, it can present different interfaces and different actions.
This is why agent-first devices are strategically interesting. They do not need to replace laptops or phones immediately. Their value may come from creating focused work surfaces for frontline workers, enterprise teams, field staff, healthcare environments, retail operations, and service workflows.
The rise of just-in-time interfaces
A traditional app has a fixed interface. A just-in-time AI interface can change based on the task. For example, a retail associate may see product lookup, customer preference, inventory, and return policy actions only when those options are relevant. A field technician may see diagnostic steps, documentation, replacement part history, and reporting actions at the moment of repair.
This matters because many business tools are overloaded. They contain too many menus, fields, permissions, dashboards, and workflows. AI agents can potentially reduce interface complexity by showing the next useful action instead of forcing users through the full software structure.
What this means for AI tool builders
AI tool builders should pay attention to this direction because the interface layer is changing. A product may no longer be judged only by its dashboard. It may need to expose actions, context, APIs, permissions, and workflow states so agents can use it effectively.
This creates opportunities for tools that are agent-ready by design. Products with clean APIs, strong authentication, modular workflows, and clear task states may become easier for AI agents to operate. Products that remain closed, rigid, or difficult to automate may feel outdated.
