AI-Driven Operating Systems

• AI-driven operating systems are adaptive environments that integrate machine learning, LLMs, and autonomous agents to replace static heuristics with dynamic, human-interactive processes.
• They leverage kernel-level AI integration, agent-based abstraction, and natural language interfaces to optimize scheduling, security, and user workflows.
• Applications span robotics, edge computing, and federated systems, showcasing enhanced performance metrics and emerging architectural paradigms.

AI-driven operating systems integrate artificial intelligence—principally ML, LLMs, and autonomous agents—across the system software stack, extending from kernel subsystems to user-facing interfaces, and replacing static heuristics with adaptive, data-driven and human-interactive processes. Unlike traditional operating systems, which rely predominantly on fixed algorithms and direct user or developer instruction, AI-driven operating systems employ components that learn, plan, and act with varying degrees of autonomy, and are often orchestrated via agent-based abstractions that bridge human intent and machine execution in natural language or multimodal form. Three principal paradigms seen in recent literature include: (1) kernel-level AI integration for resource management and security; (2) agent-mediated user and system workflows leveraging multimodal LLMs; and (3) operating systems abstracted or supervised by LLM-based agents serving as a new form of OS "kernel" (Zhu et al., 15 Sep 2025, Kang et al., 30 May 2025, Tan et al., 28 Nov 2024, Packer et al., 2023, Hu et al., 6 Aug 2025, Ge et al., 2023).

1. Foundational Principles and Core Architectures

AI-driven operating systems exhibit three convergent trends: embedding AI for adaptive resource management, abstracting interfaces for agent-driven orchestration, and using natural language as a primary programming and interaction modality.

Kernel- and System-layer AI Integration:

In low-level domains, ML models are used for fundamental OS functions such as scheduling, memory management, and security (Safarzadeh et al., 2021, Zhang et al., 19 Jul 2024). Examples include using reinforcement learning for CPU and I/O scheduling, supervised models for workload classification, and lightweight neural inference for security anomaly detection. The Composable OS Kernel architecture incorporates loadable kernel modules (LKMs) as in-kernel AI computation units for direct sensory inference, with user space–kernel interaction mediated through custom syscalls, and neural-symbolic reasoning operators embedded using category theoretic formalisms (Singh et al., 1 Aug 2025).

Agent-based Abstraction Layers:

Agent-enabled OSes, such as MedicalOS, adopt a multi-layered architecture. The Agent–Computer Interface exposes user intent via natural language to an LLM agent that decomposes workflows into domain-specific command abstractions (e.g., a medical programming language, MPL), which are then orchestrated into concrete tool invocations (Python APIs, shell commands, HL7/MCP calls). The agent proactively sequences, validates, and audits these actions, interacting with users via ReAct-style (reasoning and acting) prompts and successively invoking tools through a strict, schema-validated interface (Zhu et al., 15 Sep 2025).

LLM-supervised and Language-mediated OSes:

A complementary paradigm, exemplified in the AIOS and Prompt-to-OS (P2OS) visions, recasts the LLM as the OS kernel. Here, the LLM arbitrates system calls, memory management (context window as working memory), persistent storage (retrieval-augmented vector stores as file systems), tool invocation, and agent execution. The interface to users and apps is natural language or multimodal (speech, text, image), and programming becomes equivalent to specifying workflows through instructions or prompts (Ge et al., 2023, Tolomei et al., 2023).

2. Memory Management, Hierarchical Storage, and Long Context

Memory management in AI-driven OSs blends classic computer architecture concepts—FIFO, LRU, segmentation, paging—with semantic, contextual, and personalized memory indexing for LLM agents and conversational systems.

Hierarchical Memory Architectures:

Systems such as MemoryOS and MemGPT generalize operating system–style memory hierarchies (fast–slow tiers, paging, and virtual memory) to the LLM and agent context management problem (Kang et al., 30 May 2025, Packer et al., 2023). The main-layer context holds hot facts and dialogue, periodically swapped with external archives. MemoryOS formalizes transitions across short-term memory (STM; FIFO buffer per dialogue turn), mid-term memory (MTM; segmented paging with heat-based eviction), and long-term personal memory (LPM; profile and knowledge base). Retrieval involves composite scoring (cosine similarity, Jaccard index) to select relevant historical segments. Functionally, this enables LLMs to maintain multi-session coherence and personalization far beyond architectural context limits, as seen in LoCoMo and NaturalQuestions–Open benchmarks, with up to 49% lift in F1 (Kang et al., 30 May 2025, Packer et al., 2023).

Virtual Context Management:

MemGPT elaborates "virtual context" management: the agent pages in relevant context slices via vector search, summarizes or compresses rolling windows for eviction and recall, and employs an event-driven control flow conceptually analogous to OS kernel interrupts—enabling the LLM to dynamically schedule planning, reasoning, and tool calls (Packer et al., 2023). This architecture matches or exceeds baseline LLMs in multi-session or nested-key retrieval and document QA tasks by enabling unbounded working memory at bounded latency.

3. Agent-based Interfaces, LLM Planning, and Command Abstractions

Agent frameworks are now central to AI-driven operating systems, spanning from GUI and user workflow automation to specialized verticals like digital healthcare, telco orchestration, and robotics.

LLM Planning and Action Grounding:

Modern OS agent systems utilize LLMs (or multimodal LLMs, MLLMs) as planners that decompose high-level goals using chain-of-thought (CoT) or ReAct (Reasoning + Acting) prompting patterns, emitting structured plans or direct command sequences (Zhu et al., 15 Sep 2025, Hu et al., 6 Aug 2025). The grounding module maps these actions to atomic OS operations, such as shell invocations, GUI events, or API calls. User feedback, tool return values, and environment state (e.g., screenshots, DOMs) are looped into the perception subsystem for iterative, closed-loop control.

Multi-agent and Modular Composability:

Systems such as ColorAgent and CognitiveOS embed modular, multi-agent architectures: a core execution or planner module interacts with orchestrators, knowledge retrievers, memory, and hierarchical reflection components; each module may be individually configured or replaced. Task decomposition, knowledge retrieval, and error diagnosis are handled collaboratively, with trajectories modified in light of prior outcomes. Reinforcement learning, self-evolving training, and retrieval-augmented action ensure performance and personalization, as evidenced in robust Android automation and robotics benchmarks (Li et al., 22 Oct 2025, Lykov et al., 29 Jan 2024).

Domain-specific Command Languages:

Domain specialists (e.g., clinicians in MedicalOS) interact with the system using high-level natural language, which the agent maps to a compact medical programming language (MPL) or similar DSL. The dispatcher module guarantees that only whitelisted commands are executed, enforces schema validation, and maintains complete audit logs for compliance—demonstrating the critical role of safe abstraction layers in high-stakes domains (Zhu et al., 15 Sep 2025).

4. Applications in Robotics, Edge, Cloud, and Federated Systems

AI-driven OSes are diversifying beyond general-purpose computing, appearing in robotics, edge/IoT, telecommunications, and real-time aviation.

Distributed Robotics and Automation:

CognitiveOS and CyberCortex.AI exemplify distributed, multi-modal agent OSes for robotics, where agents coordinate sensor processing, planning, action execution, and ethical constraint satisfaction via internal monologue protocols (Lykov et al., 29 Jan 2024, Grigorescu et al., 2 Sep 2024). DataBlock (CyberCortex.AI) or multi-agent transformer structures (CognitiveOS) provide modular scheduling of perception and control, hybrid local/cloud learning pipelines, and persistent memory across robot swarms. Empirical results show substantial improvements in reasoning, symbol understanding, and precision over prior cognitive robotics OSes.

Edge and Federated AI Operating Systems:

Horizontal federated AI OS platforms for telecommunication are designed with explicit orchestration, coordination, and privacy domains, supporting lifecycle management and agent execution across edge nodes with regulatory isolation and integration with industry standards (TM Forum, O-RAN). Abstractions such as telemetry ingestion APIs, feature stores, federated training rounds, and secure aggregation interfaces provide the backbone for agent-based automation in distributed, heterogeneous operator landscapes, with documented gains in communication efficiency, time-to-convergence, and rollout speed (Barros, 9 Jun 2025).

Real-time Embedded Operating Systems:

In safety-critical, resource-constrained domains, AI-driven OS architectures employ dynamic resource management, preemptive interrupt handling, and modular component isolation. For example, the OrinFlight OS operates on NVIDIA Jetson Orin hardware, providing synchronized distributed processing, priority-based CPU/GPU scheduling, security protocols (AES-GCM, SELinux), and fault tolerance (watchdog daemons), and exposes a low-code orchestration layer for rapid mission reconfiguration in drone fleets (Tan et al., 28 Nov 2024).

5. Evaluation, Metrics, and Safety Considerations

Empirical validation in AI-driven OSes requires multi-faceted metrics:

Diagnostic and Functional Metrics:

• Success rates in end-to-end automation tasks (AndroidWorld, OfficeBench, MiniWoB) (Hu et al., 6 Aug 2025, Li et al., 22 Oct 2025).
• Diagnostic accuracy (cosine similarity of embedding between agent and ground truth), self-reported confidence, and test-driven robustness in use cases such as clinical diagnosis (Zhu et al., 15 Sep 2025).
• Memory retention/consistency (F1/BLEU-1 in LoCoMo), efficiency (token/call count), real-time pipeline latency, and resource utilization (CPU/GPU/bandwidth overhead) (Kang et al., 30 May 2025, Grigorescu et al., 2 Sep 2024, Tan et al., 28 Nov 2024).

Security, Transparency, and Compliance:

• Strict command validation, immutable audit trails, guideline citation at each controlled action, and human-in-the-loop intervention are critical safety design patterns, especially for regulated domains (e.g., healthcare, telco) (Zhu et al., 15 Sep 2025, Barros, 9 Jun 2025).
• Encryption, access control, forensic logging, and sandboxed execution limit attack surface and ensure recoverability (Tan et al., 28 Nov 2024, Barros, 9 Jun 2025, Bleotiu et al., 2023).
• AI-social engineering, trustworthiness, and explainability remain ongoing challenges, with recommendations for transparency logs, content-sharing policies, and static analysis for prompt and tool code (Tolomei et al., 2023, Ge et al., 2023, Zhang et al., 19 Jul 2024).

6. Future Roadmaps and Open Problems

Research envisions multi-stage trajectories for AI-OS evolution (Zhang et al., 19 Jul 2024, Ge et al., 2023):

• Stage 1: AI-powered OS—Loose coupling of ML and LLM agents as plugins; isolated enhancements in schedulers, memory, or CLI copilot interfaces.
• Stage 2: AI-refactored OS—Co-designed OS subsystems with semantic prefetching, modular kernels, or microservices specialized for AI workloads.
• Stage 3: AI-driven OS—Fully agent-mediated, self-optimizing systems that replace static policies with adaptive agents, coupled with unified memory, tool, and control abstractions.

Open research areas include lightweight and verifiable in-kernel inference, federated and continual agent learning, formal safety proofs, explainability in decision pipelines, resilient agent collaboration, and user-centric permission frameworks. The intersection of LLM-based OS kernels, multi-agent orchestration, language-based programming, and regulatory compliance defines a rapidly expanding frontier for operating system research and practice (Hu et al., 6 Aug 2025, Ge et al., 2023, Zhu et al., 15 Sep 2025).

---

Key References:

• "MedicalOS: An LLM Agent based Operating System for Digital Healthcare" (Zhu et al., 15 Sep 2025)
• "Memory OS of AI Agent" (Kang et al., 30 May 2025)
• "An Integrated Artificial Intelligence Operating System for Advanced Low-Altitude Aviation Applications" (Tan et al., 28 Nov 2024)
• "MemGPT: Towards LLMs as Operating Systems" (Packer et al., 2023)
• "OS Agents: A Survey on MLLM-based Agents for General Computing Devices Use" (Hu et al., 6 Aug 2025)
• "LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystem" (Ge et al., 2023)
• "Composable OS Kernel Architectures for Autonomous Intelligence" (Singh et al., 1 Aug 2025)
• "ColorAgent: Building A Robust, Personalized, and Interactive OS Agent" (Li et al., 22 Oct 2025)
• "CyberCortex.AI: An AI-based Operating System for Autonomous Robotics and Complex Automation" (Grigorescu et al., 2 Sep 2024)
• "CognitiveOS: Large Multimodal Model based System to Endow Any Type of Robot with Generative AI" (Lykov et al., 29 Jan 2024)
• "The Case for a Horizontal Federated AI operating System for Telcos" (Barros, 9 Jun 2025)
• "Integrating Artificial Intelligence into Operating Systems: A Survey on Techniques, Applications, and Future Directions" (Zhang et al., 19 Jul 2024)
• "Artificial Intelligence in the Low-Level Realm -- A Survey" (Safarzadeh et al., 2021)
• "Prompt-to-OS (P2OS): Revolutionizing Operating Systems and Human-Computer Interaction with Integrated AI Generative Models" (Tolomei et al., 2023)
• "Naeural AI OS -- Decentralized ubiquitous computing MLOps execution engine" (Bleotiu et al., 2023)