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[ARTICLE · art-133311] src=arxiv.org ↗ pub= topic=ai-agents verified=true sentiment=· neutral

Position: It is Time to Virtualize Foundation Models with a Self-evolving Operating System Layer

A position paper posted to arXiv (2609.19203v1) argues that compound agentic AI systems need a Foundation Model Operating System (FMOS), a system layer that virtualizes foundation model interactions the way virtual machines abstract physical hardware. The paper says today's stacks remain fragmented because each framework embeds its own implicit runtime for state, memory, budgets, and guardrails, making behavior non-portable and governance brittle even as protocols such as MCP and A2A ease tool and agent connectivity. The proposed FMOS would orchestrate knowledge across memory tiers, model selection and resource allocation, and verification and policy enforcement, learning when to intervene and when to let inference proceed directly.

by read1 min views1 publishedSep 18, 2026

arXiv:2609.19203v1 Announce Type: new Abstract: AI applications have shifted from single, monolithic foundation models (FM) to compound agentic systems. Yet today's stacks remain fragmented: even as protocols (e.g., MCP, A2A) ease tool/agent connectivity, each framework embeds an implicit runtime for state, memory, budgets, and guardrails, making behavior non-portable and governance brittle. It mirrors computing before operating systems, when every program re-implemented basic services. This position paper argues that the field now needs a Foundation Model Operating System (FMOS) -- a system layer that virtualizes FM interactions analogous to how virtual machines abstract physical hardware, giving applications the illusion of dedicated, trustworthy FM instances with effectively unbounded capabilities. Internally, the FMOS orchestrates knowledge across memory tiers, model selection and resource allocation, and verification and policy enforcement. Like the human brain switching between fast intuition and slow deliberation, the FMOS learns when to intervene and when to let inference proceed directly and continuously adapting its policies based on operational experience.

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