Agentic operating systems for business

Technology and business move fast — so the system has to be flexible above all: easy to re-shape for new models, tools and processes. And Pareto-optimal — 80% of the result for 20% of the cost. Everything else is built out of that philosophy.

Our vision for bringing AI into corporate infrastructure comes down to finding the optimal way to meet a few requirements.

Unified data. All the company's data — wikis, boards, methodologies, documents, correspondence — integrated or gathered in one place and available to AI agents. It is the single source of truth everything else turns to; the task queue for autonomous agents lives here too.

Role-based access. Agents' and employees' access to data is governed by one role model. An agent under an employee's name is bound by their role; an autonomous agent has its own — a subject in its own right in the matrix.

An agent for every employee in one click. A ready agentic AI system, already set up with the company's infrastructure and data, opens with no setup — strictly within the bounds of the employee's role.

Autonomous agents. They follow instructions, pull tasks from the queue themselves and move them through the stages — routine processes run without a human at every step.

An assistant in communication. It helps across internal and external correspondence: it suggests, phrases and speeds up replies within the bounds of the employee's role, and the human decides.

A "thin" business dashboard. Real-time display of corporate data, prepared for flexible "vibe-coding" of the format: any slice is assembled for the task in hours, and the complexity stays under the hood.

Architecture

Under the hood, four layers: data, access, execution, display. Data at the bottom, a single access layer over it — and everything that acts in the system reaches data only through it.

The data layer. The organization's single memory: records, documents, playbooks, conversations — gathered here or wired in via third-party integrations. The task queue for autonomous agents lives here too. The one source everything else turns to.

The access layer. Identity and authorization in one: it recognizes the subject — employee or agent — and decides what they may do. One access matrix for all: an agent under an employee's name is bound by their role; an autonomous agent has its own — a subject in its own right in the matrix. All data access goes through it.

The execution layer. Isolated environments where agents do the work: personal, autonomous, chat assistants and the dashboard-config agent. They reach data through the access layer and models through an LLM proxy on a single subscription — it meters and logs every call, so costs stay in check and AI usage shows up in analytics.

The display layer. Thin dashboards under RBAC, vibe-code-ready by design: all the complexity is below, leaving a pure view on top — assembled in hours by the dashboard-config agent. The dashboard reads data through the same access layer, so it gives real-time visualization strictly within the viewer's role.

Roadmap