Why the AI Era Is Giving Rise to a New Kind of Operating System: Agent OS

Operating‑system fundamentals are being rewritten as a new “Agent OS”—an OS that uses tokens as fuel and agents as the interaction core—entering the spotlight in 2026, with Google, Microsoft, Alibaba, Tencent, Huawei and other tech giants all betting on this trajectory.

Why a new OS? Traditional OSes manage hardware resources, but AI agents run on “one‑off software” lacking context, scheduling, and state tracking, leading to the so‑called AI “amnesia” where each inference discards all state.

Five core pillars of Agent OS :

Authorization : Define which data and tools an AI can access and which actions require human approval.

Scheduling : Coordinate multi‑system tasks (e.g., flight booking, seat selection, payment, calendar entry) like a brain’s central scheduler.

Execution : Enable the AI to act, not just advise—e.g., automatically trigger procurement when inventory is low.

Tracking : Monitor task progress, bottlenecks, and responsible parties to move beyond mere Q&A.

Governance : Provide visibility into data sources, tool usage, and outcome compliance.

These pillars shift the OS focus from managing hardware to managing tasks, turning the OS into a “property manager” that knows who lives where, who holds the keys, and when garbage is collected.

Memory management challenges arise because AI agents lose context after each inference. Manus (2025) showed that KV‑Cache hit‑rate is the most critical metric—cache hits cost only one‑tenth of a token compared to misses—making context organization directly affect cost and latency. They also use the file system as external memory (a low‑cost virtual memory) and a Todo‑list to rehearse goals at each step, preventing goal drift.

DeepSeek identified a “U‑shaped” optimal resource split: 75‑80% of tokens for the “brain” (computation) and 20‑25% for the “book” (memory), arguing that neither unlimited context nor pure external retrieval suffices; a hierarchical memory architecture is required.

Identity and permissions determine how far an AI can go. An AI tasked with handling sales contracts must have read access to CRM data, permission to generate quotes, and authority to invoke e‑signature services—issues of identity, not model capability. Microsoft’s long‑standing ecosystem (Windows, Office, Teams, Azure, Entra ID) gives it a decisive edge in defining enterprise identity, permission, and data rules.

Industry race : Google worries that agents could bypass search, threatening its entry‑point monopoly; its Gemini project aims to defend that gate. Microsoft focuses on execution loops via its enterprise infrastructure. Apple envisions agents as a universal scheduling hub across devices. Huawei’s HarmonyOS 7 (June 2026) and Alibaba Cloud’s Agentic OS both claim native AI‑agent capabilities, integrating runtime, security, and 24/7 observability.

The common trend is a shift from “search‑for‑answers” to “delegate‑tasks”. Users will soon say, “Plan my Shanghai trip next week,” and an Agent will autonomously book flights, hotels, and calendar events, acting as a coordination layer between humans and the digital world.

In summary, Agent OS adds a new coordination layer atop traditional OSes, turning AI from a passive assistant into an autonomous executor that can manage context, permissions, and complex multi‑system workflows—signaling a fundamental transformation of how software and humans interact.