AI-Driven Operating Systems

• AI-driven operating systems are computational platforms that embed AI agents in the kernel, middleware, and interfaces to provide dynamic, context-aware resource management.
• They utilize graph-based modeling, domain-specific languages, and in-kernel ML inference to orchestrate automated scheduling, memory management, and task coordination.
• Empirical studies show these systems can improve operational efficiency, reduce manual intervention, and enhance performance in robotics, cloud computing, and cyber-physical applications.

AI-driven operating systems (AI-driven OSs) are computational platforms in which artificial intelligence—especially machine learning, LLMs, and agent-based methods—are integrated into the core architecture, enabling dynamic, adaptive, and context-aware management of resources, user interactions, and computational workflows. Unlike conventional systems where AI is an application-layer facility or add-on, an AI-driven OS weaves AI techniques through its kernel, middleware, user interface, and application abstractions, effecting automated and self-optimizing behaviors across the entire stack. These systems are variously described as agent-centric OSs, AI-native OSs, or meta-OSs, reflecting a paradigm in which resource management, scheduling, security, UI, and extensibility are continually updated, mediated, and orchestrated by intelligent modules or autonomous agents.

1. Core Architectural Principles and Patterns

Modern AI-driven OSs transcend the classical division between kernel, user space, and middleware by embedding intelligent agents or model-driven modules into every principal tier. Foundational abstractions include:

Agent-Centric Architecture: Core resource managers—schedulers, memory allocators, I/O binder, security framework—are replaced or augmented with agents that learn, adapt, and collaborate, either via ML, RL, or LLM-based reasoning. These agents expose uniform peer-to-peer messaging interfaces and can discover, compose, and negotiate without static system calls (Jia et al., 2024).

Graph-Based State and Meta-Modeling: Central workspace abstractions are modeled as attributed, typed graphs, capturing not just files and processes but tools, documents, code, and process structure. Graph meta-models serve as the operative substrate for modeling, transformation, and orchestration, naturally blending data, code, and UI structures (Ceravola et al., 2024, Ceravola et al., 2024).

Domain-Specific Languages & High-Level Semantics: Lightweight, extensible DSLs define how users or higher-level modules interact with the OS, supporting code generation, navigation, simulation, and process management. DSL constructs are linked directly to graph operations and AI triggers (Ceravola et al., 2024, Ceravola et al., 2024).

AI-Native Resource Scheduling, Memory, and Control: Scheduling, CPU/memory arbitration, and interrupt handling are managed by routines that incorporate ML for workload prediction, RL for reward-driven adaptation, and—at the kernel level—PL-guided resource usage and constraint propagation (Singh et al., 1 Aug 2025, Safarzadeh et al., 2021, Zhang et al., 2024).

Transactional and Provenance-Aware Execution: Laboratory and cyber-physical OSs employ transactional models (e.g., CRUTD, an extension of CRUD that includes atomic transfer under ACID semantics) to reconcile digital plans with physical execution and to enable reproducible, auditable operation (Gao et al., 25 Dec 2025).

2. AI Integration Models, Methodologies, and Kernel Modifications

Integration of AI within the OS spans several technical models:

In-Kernel and Module-Based Inference: Loadable kernel modules (LKMs) and function hooks are repurposed and extended as AI-oriented compute units. ML inference runs inside or alongside the kernel (e.g., quantized NNs for I/O queueing, RL agents for cache eviction or memory tuning). ML-aware scheduling classes and floating-point/GPU drivers are introduced directly into core kernel routines, enabling low-latency, predictable ML pipelines (Singh et al., 1 Aug 2025, Safarzadeh et al., 2021, Zhang et al., 2024).

User-Level and Hybrid Agent Frameworks: At the user or system-agent level, OSs embed LLMs or multi-agent planners that translate natural language or high-level instructions into atomic OS actions, automate process orchestration, and generate new code or configurations. This is typified by assistant agents (e.g., ColorAgent), modular robot cognitive managers (CognitiveOS), or agent-centric shells mediating user and system interaction (Li et al., 22 Oct 2025, Lykov et al., 2024, Jia et al., 2024).

Graph Engine and Transformation Pipelines: Every OS service (window spawning, code generation, document linking, code execution) is implemented via graph-rewriting operations or pattern-matching transformations on the workspace graph. Operations can be triggered either by direct user manipulation or as a result of AI/LLM-driven code and workflow generation (Ceravola et al., 2024, Ceravola et al., 2024).

Memory and Context Management for LLM Integration: Memory operating systems (MemoryOS, MemGPT) introduce hierarchically managed memory tiers—short-term, mid-term, and long-term persona memory—mirroring classic OS paging as LLM context and neural scratchpad. Interrupt-driven control, virtual context management, and externalized stores enable LLMs to operate beyond their default memory window, providing coherent session management, swap, and personalized adaptation (Kang et al., 30 May 2025, Packer et al., 2023).

3. Implementation Strategies, Formulations, and Formal Semantics

Specific implementations instantiate the above principles through:

Typed, Stateful APIs and Atomic Transactions: Systems such as UniLabOS abstract every element (instrument, sample, method) as typed objects interfaced via strongly typed, transactional APIs. State changes obey atomicity, consistency, isolation, and durability (ACID), leveraging explicit transaction state machines and dual-topology resource/physical graphs (Gao et al., 25 Dec 2025).

Formal Scheduling and Resource Allocation: Task scheduling incorporates priority assignment and resource quota calculations grounded in empirical and theoretical models. For AI-invocation tasks, CPU share is set by

$\mathrm{share}(t_i) = \frac{p_i}{\sum_{j=1}^n p_j}$

and latency and throughput are estimated as

$$L_i = \alpha\frac{1}{\mathrm{share}(t_i)}, \quad \Theta = \sum_i \frac{1}{L_i}$$

(Ceravola et al., 2024). ML-oriented scheduling adapts priorities according to deviation of inference time from target latency:

$$p_i(t+\Delta t) = p_i(t) + \alpha(T_\text{target} - T_i(t))$$

(Singh et al., 1 Aug 2025).

Graph Transformations and Code Generation: Transformations are defined as

$f: G \rightarrow G'$

where subgraphs matching a left-hand side pattern $L$ are located, elements modified or replaced, and a right-hand pattern $R$ inserted. User-defined integrity rules are expressed as graph patterns $P$; pattern matching and constraint propagation are performed via efficient subgraph isomorphism (Ceravola et al., 2024).

Transactional Provenance and Recovery: All modifications are logged as atomic CRUTD operations in a distributed, versioned log. In dual-topology systems, logical and physical resource graphs are coordinated to ensure full provenance of actions and resources, supporting recovery and replay under network partition or failure (Gao et al., 25 Dec 2025).

4. Applications, Case Studies, and Performance Characteristics

AI-driven OSs have been evaluated in scientific, engineering, robotics, and cloud contexts:

Use Case	Nodes/Modules	Success Rate / Perf.	Key Notes
Avatar Dialog System	4,246	95% success	Real-world DSL for RNN dialog (Ceravola et al., 2024)
Robotic Task Planning	414	87% success	Multi-agent LLM (CoPAL) (Ceravola et al., 2024)
Thebes Research DSL	120	—	Meta-model prototyping in <1 day (Ceravola et al., 2024)
AndroidWorld (ColorAgent)	—	77.2%	Outperforms Qwen2.5-72B by >40 points (Li et al., 22 Oct 2025)
MemoryOS F1/BLEU-1	—	+49.11% / +46.18%	Retains context in long conversations (Kang et al., 30 May 2025)

Other notable empirical attributes:

• On-the-fly graph edits in HyperGraphOS propagate in <100 ms for 5,000-node workspaces; code generation of thousands of lines occurs in seconds (Ceravola et al., 2024).
• Integrating LLM-driven planning reduced manual recoding time by ≈70% and reduced experimental context switches by 50% (Ceravola et al., 2024).
• MemoryOS increases F1 and BLEU-1 by ~50% over baselines in extended dialog (Kang et al., 30 May 2025).
• Scheduler overhead in AI-native kernels remains under 10 µs, with throughput improved by up to 3.5× over traditional kernels in ML-heavy pipelines (Singh et al., 1 Aug 2025).
• Interrupt-driven LLM memory management in MemGPT supports multi-hop retrieval and sustains document QA accuracy at scaling levels unreachable by fixed-context LLMs (Packer et al., 2023).

5. Comparison with Classical and Vertical Platforms

AI-driven OSs show clear architectural and functional divergences from classical and domain-specific platforms:

Feature / Platform	HyperGraphOS	MetaEdit+	WebGME	RabbitR1/SpaceOS
Infinite, linked WorkSpaces	✓	–	–	–
Extensible DSLs (first-class citizens)	✓	✓	✓	–
Integrated LLM-based AI triggers	✓	–	–	✓ (voice)
Live execution and animation	✓	–	–	–
Multi-level modeling, web-based access	✓	–	✓	–

Relative to traditional vertical and app-based stacks, modern AI-driven OSs:

• Displace files/folders metaphors with infinite, graph-structured “OmniSpaces” (HyperGraphOS);
• Replace shell, app, and static API layers with DSL-first, AI-native interaction pipelines;
• Subsume external modeling, code, and execution toolchains under the operating system core;
• Support dynamic, in-place execution and debugging;
• Substantially accelerate DSL and model development velocity.

Horizontal federated approaches, such as Ratio1 or the Telco Horizontal Federated AI OS, further decouple abstraction layers (resource, service, application), moving orchestration, state, and learning to decentralized, blockchain-anchored frameworks (Damian et al., 5 Sep 2025, Barros, 9 Jun 2025).

6. Challenges, Limitations, and Future Directions

Several limitations and open challenges remain:

• Model Drift, Explainability, and Security: Challenges include stale or adversarial model behaviors, lack of transparency or auditability for in-kernel inference, and increased attack surfaces due to expanded kernel intelligence. Future work must address quantized, verifiable in-kernel ML, robust provenance, and dynamic guardrails (Safarzadeh et al., 2021, Zhang et al., 2024).
• Real-Time and Resource Overhead: Guaranteeing real-time deadlines amid dynamic ML inference, as well as scaling to high-concurrency deployments, remains a critical bottleneck, especially in safety-critical robotics or cyber-physical domains (Tan et al., 2024, Grigorescu et al., 2024).
• Extensibility and Upgrade: Complete decoupling of AI agent updates, DSL expansions, and meta-model changes with continuous safety and compatibility is not fully resolved; modularity and semantic versioning help but do not eliminate cross-layer migration costs (Ceravola et al., 2024).
• Standardization and Ecosystem: No de facto standard yet exists for inter-agent communication, agent marketplace, or cross-device trust and synchronization. There is a call for unified toolchains, benchmarks, and modular kernel APIs (Zhang et al., 2024, Jia et al., 2024).
• Federated and Decentralized Governance: The emergence of protocol-level, blockchain-backed meta-OSs introduces challenges of consensus latency, governance, legal compliance, and seamless adaptation to network heterogeneity (Damian et al., 5 Sep 2025, Barros, 9 Jun 2025).
• Transition Metrics and Hybridization: Deciding when to move from AI-powered to AI-refactored to AI-driven architectures requires formal metrics balancing performance uplift, complexity, and maintainability (Zhang et al., 2024).

The field continues to evolve rapidly, with future research focusing on scalable, agent-based orchestration; modular, transactionally safe extension; in-kernel and edge-compatible AI; and dynamic, secure, and explainable operation across diverse environments.