发表机构
University of the Peloponnese; Cornell University（伯罗奔尼撒大学; 康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出智能体AI的分层架构，分析OpenClaw与Ollama组成的全栈系统，验证系统集成可提升智能体能力，指出相关挑战并提供路线图，所有资源公开以支持研究。
AI 中文摘要
从反应式大语言模型（LLM）向具备行动能力的持久化系统的快速过渡，暴露出智能体AI在架构理解方面的关键缺口，尤其是自主AI智能体的推理、编排与执行层的分离问题。尽管近期取得了一些进展，但用于设计和评估全栈智能体系统的统一框架仍然有限。本文提出了一种用于智能体AI的综合性分层架构，概述了从反应式LLM接口向具备记忆、规划和持续执行能力的持久化、目标驱动型自主AI智能体的演进过程。我们将OpenClaw和Ollama作为一个全栈智能体AI系统进行分析，其中Ollama作为LLM推理层，OpenClaw则作为智能体运行时编排层，集成了推理、工具使用和行动执行功能。对OpenClaw-Ollama架构的原型实验验证表明，持久化记忆、工具利用和自适应决策等能力源于系统级集成，而非单个模型，且随着架构复杂度的提升，性能持续改善。本研究进一步探讨了智能体系统在可扩展性、安全性、隐私性、治理和评估方面的挑战，强调需要建立可靠的基准测试和系统级设计。未来方向包括可扩展的多智能体架构、分布式自主系统以及面向负责任部署的以人为中心的智能体AI框架。总体而言，这项工作为智能体AI奠定了统一的架构基础，验证了全栈自主AI智能体的有效性，并为构建可扩展、安全且可信的智能体系统提供了路线图。所有模型、代码和数据集均已公开发布，以支持可复现性和基准测试。
英文摘要
The rapid transition from reactive large language models (LLMs) to persistent, action-capable systems has exposed critical gaps in the architectural understanding of Agentic AI, particularly in separating inference, orchestration, and execution layers for autonomous AI agents. Despite recent advances, unified frameworks for designing and evaluating full-stack agentic systems remain limited. This paper presents a comprehensive, layered architecture for Agentic AI, outlining the evolution from reactive LLM interfaces to persistent, goal-driven autonomous AI agents with memory, planning, and continuous execution. We analyze OpenClaw and Ollama as a full-stack Agentic AI system, where Ollama serves as the LLM inference layer and OpenClaw enables agent runtime orchestration, integrating reasoning, tool use, and action execution. A prototype experimental validation of the OpenClaw-Ollama architecture demonstrates that capabilities such as persistent memory, tool utilization, and adaptive decision-making emerge from system-level integration rather than standalone models, with performance improving consistently as architectural complexity increases. The study further examines challenges in scalability, security, privacy, governance, and evaluation of agentic systems, highlighting the need for robust benchmarking and system-level design. Future directions include scalable multi-agent architectures, distributed autonomous systems, and human-aware Agentic AI frameworks for responsible deployment. Overall, this work establishes a unified architectural foundation for Agentic AI, validates the effectiveness of full-stack autonomous AI agents, and provides a roadmap for building scalable, secure, and trustworthy agentic systems. All models, code, and datasets are publicly released to support reproducibility and benchmarking.
