{"id":"91272a1e-7576-4285-92f3-f7255864a786","arxiv_id":"2606.01508","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":3.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Proposes the Agent Operating System (AOS) as a systems architecture integrating an agentic control plane into existing OS or subsuming selected responsibilities to support long-lived, adaptive AI agents.","lead":"The paper proposes an Agent Operating System (AOS) architecture that adds an agentic control plane to handle goal-directed AI agents within or beyond traditional operating systems. A smart generalist might read it to understand potential foundations for secure, auditable AI agent execution at scale.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"Reader's weakest assumption concerns motivation for needing AOS beyond user-space, but the strongest claim is simply the introduction of the concept itself. For a conceptual proposal paper, this motivation does not constitute a load-bearing correctness risk to the claim. No internal inconsistency or unsubstantiated formal result is present in the described structure.","tokens_in":1770,"tokens_out":274,"duration_ms":16544,"concrete_test":"Confirm the manuscript contains the promised sections: precise AOS definition, explicit assumptions/non-goals, structured decomposition into schedulers/memory/tool registries/policy/observability, analysis of classical OS limitations, integration models, Linux/Windows mappings, and evaluation criteria. If these are present and internally coherent, the proposal claim is satisfied.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central claim is the introduction of a conceptual AOS architecture, including a definition, responsibility decomposition, integration models, and mappings to existing OS primitives. This is a definitional and architectural proposal rather than an empirical or formal result. The motivation (agentic workloads stressing classical abstractions beyond user-space) is asserted but does not need to be proven for the claim of 'introducing the concept' to hold; the paper fulfills its stated objective by providing the listed elements.","agreement_with_reader":"disagree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript introduces the concept of an Agent Operating System (AOS) as a systems architecture integrating an agentic control plane into (or beyond) traditional operating systems. It supplies a definition of AOS together with explicit assumptions and non-goals, decomposes AOS responsibilities into schedulers, context/memory management, tool/capability registries, policy/trust enforcement, and observability/audit, analyzes limitations of classical OS abstractions for agentic workloads, proposes integration models ranging from user-space runtimes to distributed control planes, maps the concepts onto Linux and Windows primitives, discusses agent-specific security and safety implications, and defines evaluation criteria centered on deterministic enforcement, auditability, and operator comprehensibility. The stated objective is to establish a rigorous systems foundation rather than to replace existing operating systems.","tokens_in":1854,"tokens_out":420,"duration_ms":25917,"significance":"If the definitional and architectural elements hold, the work supplies a structured starting point for future systems research on controllable, accountable agentic computation. Credit is due for the explicit responsibility decomposition, the concrete mapping of AOS concepts onto existing OS primitives, and the formulation of agent-specific evaluation criteria; these elements are load-bearing for the paper's claim of providing a 'rigorous systems foundation.'","major_comments":[],"minor_comments":[{"comment":"Abstract: the single-paragraph abstract introduces many distinct contributions (definition, decomposition, integration models, mappings, threat models, evaluation criteria) without visual separation; a short bulleted enumeration of the main contributions would improve scannability while preserving the paragraph form for the motivation.","section":null},{"comment":"The manuscript asserts that agentic workloads stress classical OS boundaries 'beyond what can be handled by implementing agents as user-space applications,' yet supplies no concrete workload trace, scheduling example, or memory-access pattern to illustrate the claimed stress points; adding one illustrative scenario (even if hypothetical) would strengthen the motivation section without altering the definitional nature of the central claim.","section":null}],"recommendation":"minor_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the positive and accurate summary of the manuscript, including recognition of the responsibility decomposition, concrete mappings to existing OS primitives, and agent-specific evaluation criteria as load-bearing elements. The recommendation for minor revision is noted. As the report lists no specific major comments, we have no point-by-point items to address.","responses":[],"tokens_in":1351,"tokens_out":84,"duration_ms":20966,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main takeaway is that the authors define an Agent Operating System as a control plane for goal-directed AI agents and break down its responsibilities into schedulers, memory management, tool registries, policy enforcement, and observability. They also sketch integration paths from user-space runtimes up to fuller replacement of OS functions and map the concepts onto Linux and Windows primitives.\n\nWhat the paper does reasonably well is the structured decomposition and the explicit listing of non-goals and evaluation criteria centered on auditability. The security implications section identifies agent-specific threat models that follow logically from the probabilistic and tool-using nature of the workloads. These elements give readers a shared vocabulary even if nothing is built yet.\n\nThe soft spot is the central motivation. The claim that agents stress classical abstractions beyond what user-space applications can handle is asserted without measurements, failure cases, or comparison to existing mechanisms such as containers, eBPF hooks, or resource controllers. No prototype or even pseudocode appears, so the proposal stays at the level of a wishlist. The mappings to existing OS primitives are useful for illustration but do not demonstrate gaps that require new abstractions.\n\nThis paper is for researchers already working on infrastructure for agentic AI who want a starting framework for discussion. It will not change how anyone ships systems today. I would bring it to a reading group on AI-systems topics as a prompt for debate rather than as settled work. I would not cite it in the next year because it introduces no verifiable mechanisms or results.\n\nIt deserves peer review in a venue that accepts position or vision papers, with the clear understanding that reviewers will press for concrete grounding or a small implementation before acceptance.","headline":"This is a definitional position paper on Agent Operating Systems that organizes ideas but supplies no implementation, data, or proof that current OSes fall short.","tokens_in":2300,"tokens_out":407,"would_cite":false,"duration_ms":18863,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"An Agent Operating System integrates an agentic control plane into traditional operating systems to manage long-lived, adaptive AI agents.","keywords":["agent operating system","agentic control plane","operating systems","AI agents","system security","observability","scheduling","policy enforcement"],"falsifier":"A large-scale deployment of agentic AI workloads that maintains deterministic scheduling, secure isolation, comprehensive auditability, and operator oversight using only existing user-space application mechanisms on current operating systems would falsify the need for an integrated AOS control plane.","tokens_in":2672,"feed_emoji":"💻","tokens_out":752,"duration_ms":22477,"temperature":0.7,"pith_summary":"The paper establishes that traditional operating systems, designed for deterministic programs with predictable interactions, face limitations when handling agentic AI systems that reason probabilistically, invoke tools dynamically, and adapt based on feedback. It defines an Agent Operating System (AOS) as an architecture that adds or subsumes an agentic control plane, with explicit decomposition into schedulers, memory management, tool registries, policy enforcement, and observability. The work analyzes how classical abstractions fall short for these workloads and proposes integration models from user-space to distributed planes while mapping concepts to Linux and Windows. A reader would care because the approach aims to ensure agentic computation remains controllable, accountable, and secure rather than relying solely on user-space implementations.","feed_headline":"Agent operating systems add control planes for adaptive AI","feed_subtitle":"Traditional OS assume deterministic programs, but agents reason probabilistically and adapt, stressing boundaries in scheduling, memory, sec","key_machinery":"The agentic control plane, which decomposes AOS responsibilities into schedulers, context and memory management, tool and capability registries, policy and trust enforcement, and observability and audit.","core_discovery":"The paper claims that an Agent Operating System (AOS) integrates an agentic control plane into existing operating systems or subsumes selected responsibilities over time, because agentic AI introduces execution characteristics that stress OS boundaries in scheduling, memory and state management, security, observability, and governance beyond user-space applications, and it supplies a definition, assumptions, non-goals, structured decomposition of responsibilities, limitation analysis, integration models, mappings to Linux and Windows, security implications with agent-specific threat models, and evaluation criteria emphasizing deterministic enforcement, auditability, and operator comprehensib","pith_inferences":["Production systems running many coordinated agents may require shared governance layers that current per-process models do not supply.","Hardware or kernel extensions optimized for long-running probabilistic execution could follow from the control-plane requirements.","Audit and policy mechanisms defined for AOS might apply to other adaptive systems such as robotic control or automated trading platforms."],"forward_implications":["Agent-specific threat models become necessary for security and safety analysis.","Evaluation criteria must prioritize deterministic enforcement, auditability, and operator comprehensibility over traditional metrics.","Integration models range from user-space runtimes to distributed control planes that may subsume OS responsibilities.","Classical abstractions for processes, threads, system calls, files, and permissions require analysis for their limits with probabilistic agent behavior.","Mappings of AOS concepts onto Linux and Windows primitives provide concrete starting points for implementation."],"fun_headline_variants":["Agent operating systems integrate agentic control planes","Integrating agentic control planes into traditional OS","AOS handles scheduling for probabilistic AI agents","Security and audit for agent operating systems"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Agentic AI systems introduce execution characteristics that stress OS boundaries in scheduling, memory and state management, security, observability, and governance beyond what can be handled by implementing agents as user-space applications.","fun_headline_variants_meta":{"raw":{"variants":["Agent operating systems integrate agentic control planes","Integrating agentic control planes into traditional OS","AOS handles scheduling for probabilistic AI agents","Security and audit for agent operating systems"]},"model":"grok-4.3","cost_usd":0.009671,"raw_usage":{"total_tokens":4357,"prompt_tokens":762,"num_sources_used":0,"completion_tokens":53,"cost_in_usd_ticks":96712000,"prompt_tokens_details":{"text_tokens":762,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":3542,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":762,"tokens_out":53,"duration_ms":28208,"temperature":1.0,"reasoning_tokens":3542,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-28T14:31:46.769298+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A large-scale deployment of agentic AI workloads that maintains deterministic scheduling, secure isolation, comprehensive auditability, and operator oversight using only existing user-space application mechanisms on current operating systems would falsify the need for an integrated AOS control plane.","supporting_citations":[],"review_version":1}