Enterprise AI Agents,
Built to Scale.

An AI fabric is a governed execution layer that sits between an enterprise's AI agents and its data, models, and tools. Rather than every AI initiative building its own connectors, security model, and model plumbing, the fabric provides those once and every agent uses them. It decides where each unit of work should run, enforces what each agent is permitted to do, keeps data in its home jurisdiction, and records everything for audit. The result is that AI stops being a sequence of independent projects and becomes a compounding platform investment.

Open agent frameworks give you orchestration primitives and leave routing, governance, and economics as an exercise for the integrator. They are a toolkit, not a foundation. The Cylix AI Fabric is opinionated exactly where it matters, in the routing decision and the policy substrate, and deliberately implementation-agnostic about which models, clouds, and stores plug in underneath. You keep your model freedom. You give up the requirement to invent the hard parts yourself.

Managed serverless is the fastest way to a first capability and the most expensive way to a hundredth. Every unit of work is metered at the provider's price, data is drawn toward the provider's cloud, and cost scales one to one with usage with no owned floor. The fabric owns the predictable compute floor, keeps data in-region, and does not route inexpensive work to metered frontier endpoints in the first place. At small scale the difference is modest. At enterprise volume it is the whole ballgame.

No. Your data platforms, ERP, warehouses, and SaaS systems remain your systems of record. The fabric connects to them over encrypted channels, processes in-region, and returns compact result sets. It does not hoard your data estate. Where the fabric's connector layer overlaps with an integration platform you already run, the sensible answer is staged consolidation rather than a forced cutover, and specialized functions such as trading-partner management can stay exactly where they are for as long as they earn their keep.

Two layers, and both are needed. First, each agent's authority is bounded when it is built, so an agent made for invoice extraction has no capability to take a payments action and no instruction, however cleverly crafted, can grant it one. The authority simply does not exist to be abused. Second, each agent registers as a first-class object in your enterprise directory, so it is visible, reviewable, and revocable from the same identity plane that already governs your people and services. Bounded authority makes an agent hard to misuse from the inside. Directory registration makes it impossible to operate in the dark from the outside.

It depends on the shape of your workload, your residency constraints, and how much of your compute floor is predictable enough to be worth owning. The structural property is that fabric cost grows sub-linearly with agent activity, because most incremental work lands on paths that are nearly free at the margin, so the advantage compounds precisely as you expand your AI footprint. We model total cost of ownership honestly against your actual numbers in the first engagement, including the scenarios where a managed platform is the better answer at your scale. You will get the real number, not the flattering one.

That is not the design intent and it is not what the architecture is good at. The division of labour is deliberate: your people keep the judgment and the critical thinking, and the fabric runs the processes. What changes is that your teams stop spending their day moving information between systems, chasing status, and rekeying data, and start spending it on the decisions only people should make. The practical effect is throughput that grows without a proportional increase in headcount, and staff working at the top of their capability rather than the bottom of it.