From Infrastructure-as-Code to AI-Native Operating Systems
A new generation of enterprise AI platforms is turning cloud infrastructure modernization into an AI-first problem. Instead of treating AI as a layer on top of legacy stacks, companies like Dust, Zerops, and Monaco are re-architecting the stack itself around agents. Their thesis is that traditional infrastructure-as-code and environment tiering were built for human-driven workflows. In an era of autonomous and semi-autonomous systems, those assumptions break. AI-native operating systems invert the model: agents become the primary actors, and infrastructure becomes a programmable substrate that guarantees consistency, observability, and governance. This shift is visible in recent AI infrastructure funding rounds that back platforms designed not just to host models, but to orchestrate fleets of agents that collaborate with humans and with each other. The result is a move away from brittle pipelines and tool sprawl toward unified, agent-first architecture across development, operations, and go-to-market.
Dust and the Rise of the Multiplayer Enterprise AI OS
Dust positions itself as a multiplayer operating system for enterprise AI, designed to make organizations, not just individuals, more intelligent. Its platform lets teams deploy, orchestrate, and govern specialized AI agents that share projects, context, conversations, notifications, and goals in a single workspace. Dust reports more than 300,000 deployed AI agents with around 70% weekly active usage, a signal that enterprises are moving beyond single-user assistants toward system-level collaboration between humans and agents. An intelligence layer connects over 100 data sources and existing tools so agents can act with rich company context. Built-in memory and reinforcement loops allow these agents to improve over time, while enterprise governance adds granular permissions, monitoring, and full audit trails. Backed by a USD 40 million (approx. RM184 million) Series B, Dust exemplifies how enterprise AI platforms are evolving into persistent operating environments, not just chat interfaces.
Zerops: Collapsing Dev and Prod for Human and AI Coders Alike
Zerops is rebuilding cloud architecture for an era where AI coding agents ship production systems on day one. Its platform-as-a-service removes the traditional separation between development and production environments, a long-standing source of deployment failures for both developers and AI-generated code. On Zerops there are no environment tiers; applications live inside a single project where code behaves identically from first commit through large-scale production. That means build, test, and deploy all happen under truly identical conditions, turning deployment into a one-click operation instead of a weeks-long exercise in configuration drift. Running on its own bare-metal infrastructure, Zerops executes workloads in full Linux containers and bundles more than 15 built-in services, from databases to messaging. By keeping applications in one consistent environment even as they scale, Zerops aligns perfectly with AI-native operating systems that expect deterministic, frictionless paths from generation to production.
Monaco and the Agentic Operating System for Revenue Teams
While Dust and Zerops reimagine core infrastructure, Monaco applies the AI-native operating system concept to sales and go-to-market. Its AI-native platform replaces a patchwork of CRM, prospecting databases, sequencing tools, and forecasting systems with an integrated, agentic environment. Within Monaco, autonomous agents help build total addressable market lists, execute outbound, capture and enrich interactions, and move deals through the pipeline with less manual overhead. The company has raised a USD 50 million (approx. RM230 million) Series B, bringing total funding above USD 85 million (approx. RM391 million), and reports adding seven figures of ARR in each of its first three months post-launch. This performance suggests strong market validation for unified sales platforms that act as a system of action, not just a system of record. For revenue teams, Monaco effectively becomes an operating system where AI and humans coordinate daily GTM execution.
A Unified, Agent-First Future for Cloud Software
Taken together, Dust, Zerops, and Monaco highlight how AI infrastructure funding is now flowing to platforms that treat agents as first-class citizens. Dust turns enterprise knowledge and workflows into a multiplayer agent environment. Zerops eliminates development-to-production friction so human and AI coders can reliably ship into identical runtime conditions. Monaco unifies prospecting, execution, and pipeline management into an agentic GTM system. All three reduce boundaries that used to be considered fundamental: dev versus prod, tooling silos across sales, and isolated AI assistants versus organizational systems. As enterprises pursue cloud infrastructure modernization, the differentiator is shifting from raw compute and configuration to AI-native operating systems that offer shared context, governed access, and seamless execution. In this model, software is no longer just deployed on infrastructure; the infrastructure itself behaves like an intelligent, collaborative platform that continuously co-operates with human teams.