arXiv:2606. 01508v1 Announce Type: cross Abstract: Traditional operating systems were designed around deterministic programs, explicit control flow, and human initiated workflows.
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Traditional operating systems were designed around deterministic programs, explicit control flow, and human initiated workflows. Their core abstractions processes, threads, system calls, files, and permissions assume bounded behavior and predictable interaction patterns.
arXiv:2608. 03214v1 Announce Type: new Abstract: Large language models have transformed artificial intelligence from isolated prediction services into components of long-running, distributed systems that reason, invoke tools, retrieve external state, delegate tasks, and act on behalf of users and organizations.
AgentKernel proposes a trust‑native operating system for AI agents, arguing that current governance layers are insufficient because they share the same process trust boundary as the agents. The OS introduces a mandatory enforcement boundary organized into four pillars—Identity, Perception, Cognition, and Execution—each adapting classical OS security principles to address semantic‑level failures such as prompt injection, memory poisoning, and tool misuse. By wrapping the agent lifecycle in this structured, non‑bypassable framework, AgentKernel aims to provide a unified security layer that can enforce identity, input mediation, memory governance, and execution control across the entire agent lifecycle.
Large language model (LLM) agents are evolving from request-response assistants into long-running software actors: they maintain state across model calls, fork subtasks, wait for external events, request human authority, generate tools, and perform side effects that must be resumed and audited. This paper presents Agent libOS, a library-OS-inspired runtime substrate for LLM agents.
arXiv:2606. 03895v1 Announce Type: cross Abstract: Large language model (LLM) agents are evolving from request-response assistants into long-running software actors: they maintain state across model calls, fork subtasks, wait for external events, request human authority, generate tools, and perform side effects that must be resumed and audited.
arXiv:2608. 13574v1 Announce Type: new Abstract: LLM agents increasingly operate as execution systems that invoke tools, modify local state, use persistent memory, and interact with external protocols.