---
title: Agent-Centric OS Architectures (ACOS)
url: https://www.emergentmind.com/topics/agent-centric-os-architectures-acos
type: topic
---

# Agent-Centric OS Architectures (ACOS)

Agent-Centric OS Architectures (ACOS) redefine the traditional operating system (OS) paradigm by positioning agents—typically Large Language Models (LLMs) or vision-language models augmented with domain knowledge—as the principal actors managing reasoning, decision-making, and execution at the core of the OS. Rather than focusing on passive, statically-defined system calls and human-centric interaction paradigms, ACOS frameworks centralize control, memory, planning, and resource allocation within a modular, agent-driven ecosystem. This approach underpins the development of intelligent, context-aware, and proactive operating systems, as shown in architectures ranging from the AIOS model (“LLM as OS”), multi-agent personalized OS agents on mobile platforms, to agent-driven workload scheduling in kernel-space environments [2312.03815] [2510.19386] [2509.01245].

## 1. Formal Framework and Architectural Principles

ACOS is characterized by the reimagining of canonical OS components as agent-mediated modules. The LLM or multimodal foundation model, functioning as the "kernel," replaces traditional monolithic kernel abstractions. Key correspondences include:

- **Kernel** → LLM core services, exposed via natural language and tool-invocation primitives;
- **Memory management** → Context window allocation and dynamic history tracking;
- **File system** → Long-term vector storage, embedding retrieval, and retrieval-augmented generation;
- **Devices/peripherals** → Modular APIs, tool-drivers, sensor/actuator integrations;
- **Middleware/libraries** → Specialized toolchains and API services linkable at runtime;
- **User commands** → Natural-language programmatic interfaces or structured prompt templates.

Within this framework, an action performed by the agent-kernel is formalized as $p(a_i|h,q; \theta)$, with $h$ as the current history, $q$ as the current prompt or sub-task, and $a_i$ as candidate actions. Memory management is achieved through relevance-driven context window selection: $H' = C(H, q) = \arg\max_{S \subseteq H, |S| \leq T_{\max}} \text{relevance}(S, q)$[2312.03815].

ACOS enforces agent instantiation, resource scheduling, inter-agent communication, and memory management through analogues of classic process scheduling—e.g., the round-robin or priority-based scheduler, but defined in terms of token/window allocation and agent orchestration[2312.03815].

## 2. Systems Implementations and End-to-End Workflow

ColorAgent exemplifies mobile-centric ACOS by integrating four interacting "planes": (1) Execution Module (a vision-language LLM), (2) Task Orchestration & Memory Management, (3) Knowledge Retrieval, and (4) Hierarchical Reflection & Error Recovery. The workflow proceeds as follows:

- **Task Orchestration**: Incoming user instructions are classified as simple or composite; complex tasks are decomposed into subtasks, which are processed with context propagation.
- **Knowledge Enrichment**: Each task/subtask is augmented with relevant external knowledge via a retriever module.
- **Execution via GUI-LLM**: The Execution Module processes the enriched prompt, emitting structured actions (e.g., JSON sequences) that are then executed on the OS.
- **Reflection and Correction**: Post-action, screenshots and action summaries are analyzed by hierarchical reflectors (per action, short trajectory, global session) for errors, with feedback loops for recovery if discrepancies are observed[2510.19386].

For system-level OS interaction, SchedCP demonstrates an ACOS realization focused on Linux scheduler policy optimization. The architecture is bifurcated into:

- **Control Plane (SchedCP + LLM Agent)**: Handles semantic reasoning, workload analysis, policy generation, and adaptation.
- **Data Plane (Linux kernel with sched_ext/eBPF)**: Executes agent-synthesized policies, performs resource accounting, and provides performance feedback.
- **Core Services**: Comprise a Workload Analysis Engine (performance data aggregation), a Scheduler Policy Repository (eBPF code storage and retrieval), and an Execution Verifier (safety and semantic correctness checks before policy deployment)[2509.01245].

## 3. Agent Coordination, Learning Mechanisms, and Planning

Robust collaboration and learning mechanisms underpin ACOS robustness:

- **Multi-agent Orchestration**: Agents decompose, sequence, and coordinate subtasks, dynamically revise plans with retrieved knowledge, and embed failure-recovery via reflection (see AgentCoordinator pseudocode in ColorAgent)[2510.19386].
- **Step-wise RL and Self-Evolving Training**: ColorAgent employs a two-stage methodology—(i) Step-wise reinforcement learning (RL) with group-relative policy optimization (GRPO), processing GUI interaction trajectories as single-step RL samples with rewards comprising action accuracy and format compliance; (ii) Self-evolving data generation/retraining loop, where a seed query pool is expanded, rollouts are filtered by discriminators, and high-quality data drives iterative fine-tuning[2510.19386]. The loss per iteration combines supervised and RL objectives.
- **Planning and Verification in SchedCP**: Agent workflows modularize into observation, planning/policy retrieval, execution, verification, and learning agents. All proposed eBPF scheduler code is validated through multi-stage static and dynamic analysis prior to deployment, ensuring both correctness and operational safety[2509.01245].

## 4. Personalization, Proactive Interaction, and Human-Agent Synergy

ACOS systems increasingly incorporate mechanisms for user-specific adaptation and proactive engagement:

- **Personalized Intent Recognition**: Explicit per-user knowledge bases (query→SOP mappings) and implicit user profiles (embedded from interaction trajectories) are used to rewrite and tailor incoming queries before execution[2510.19386].
- **Proactive Engagement**: "AskAgent" functionality is realized through meta-knowledge decoupling, training the system to request user clarification only when necessary, thus optimizing intent alignment and success rates[2510.19386].
- ColorAgent reports an Intent Alignment Rate (IAR) increase to 58.7% (MobileIAR) and a step-wise Success Rate of 68.98% on VeriOS-Bench with such strategies.

The AIOS model frames the user as an additional privileged agent, capable of pre-empting or supplementing agent context in mixed-initiative or conversational modes[2312.03815].

## 5. Evaluation Metrics and Benchmark Results

Empirical validation of ACOS frameworks leverages both standard and agent-specific metrics:

- **Task Success Rate (SR)**: ColorAgent shows measurable gains on AndroidWorld (AWorld) and AndroidLab (ALab) benchmarks, with incremental improvements from step-wise RL (63.0%/45.2%), self-evolving training (65.1%/48.6%), reflection (70.3%/49.5%), orchestration (72.8%/50.1%), to full knowledge retrieval (77.2%/50.7%)[2510.19386].
- **Scheduler Optimization**: SchedCP achieves up to 1.79× speedup in kernel compilation time, 2.11× reduction in p99 scheduling latency, and 20% average end-to-end time reduction in AI-generated batch workloads. The cost per workload demonstrates a 13× reduction (from $6 to $0.5), with 100% success in generating valid custom schedulers. In contrast, naïve agentic methods fell below 33% success and often produced performance regressions[2509.01245].
- **Other Metrics**: ACOS evaluation methods include tool-use accuracy, Jain fairness index for context allocation, retrieval recall@k, and adversarial robustness measures[2312.03815].

Benchmark limitations are observed, including narrow task coverage, lack of robust user-alignment and recovery metrics, and insufficient simulation of dynamic UI anti-patterns[2510.19386].

## 6. Design Principles and Research Directions

Several architectural and methodological principles have been distilled from ACOS research:

- **Modularity and Decoupling**: Separating reasoning (LLM/core agent) from system-level execution via abstracted APIs and verifiers enhances robustness and security. The modular "plane" design is consistently observed in both ColorAgent and SchedCP implementations[2510.19386] [2509.01245].
- **Interleaved Training and Lifelong Learning**: Effective ACOS frameworks blend supervised representation learning, on-policy RL, and agent-driven experience generation to bridge gaps between static benchmarks and real-world, dynamic environments[2510.19386].
- **Resource and Memory Management**: Analogous to DRAM swapping, methods for context window virtualization, memory sharing across agents, and disaggregated memory for distributed ACOS nodes are prominent research fronts[2312.03815].
- **Communication Protocols and DSLs**: Domain-specific languages for tool invocation and inter-agent plans allow for structured agent communication and enable static safety analysis.
- **Security, Alignment, and Formal Verification**: Multi-stage verification regimes—including symbolic sandboxes, static program analyses, adversarial simulation, and DSL plan validation—are critical for operational trustworthiness.
- **Generalizability**: The ACOS agent-control paradigm is applicable to systems beyond schedulers, including cache policy, DVFS governors, network queuing, and co-optimization of multi-resource environments[2509.01245].
- **Research Roadmap**: Key developmental stages trace the transition from batch LLM inference to interactive CoT, from single-agent to multi-agent collaboration, the incorporation of DSL-driven control, and robust, verified agent execution[2312.03815].

## 7. Taxonomy of Agent Applications and Ecosystems

The ACOS approach enables a broad taxonomy of agent-driven applications, including:

- **Single-Agent Systems**: Autonomous agents interacting with external APIs or physical devices for code generation, web navigation, robotics, or task automation. Control architectures often deploy CoT or more advanced graph-based planning.
- **Multi-Agent Systems (MAS)**: Specialized agents coordinate via collaborative or adversarial protocols (e.g., MetaGPT, ChatDev, Multi-Agent Debate), utilizing global blackboard architectures or debate-style feedback loops.
- **Human-Agent Interaction Modes**: Ranging from fully automated operation, to mixed-initiative workflows (user and agent interleave control), to conversational, dialog-driven interfaces. The ability to dynamically allocate control and context enables flexible adaptation to diverse OS environments[2312.03815].

The AIOS-Agent ecosystem model provides an end-to-end realization: the LLM kernel instantiates, schedules, and manages agents as primary OS abstractions, offering a distinct shift from the traditional OS–APP model.

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**References**

- "LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystem" [2312.03815]
- "ColorAgent: Building A Robust, Personalized, and Interactive OS Agent" [2510.19386]
- "Towards Agentic OS: An LLM Agent Framework for Linux Schedulers" [2509.01245]

Source: https://www.emergentmind.com/topics/agent-centric-os-architectures-acos