OpenClaw and Ollama in Agentic AI: Toward Fully Autonomous and Scalable AI Agent Systems
A new arXiv paper proposes a layered architecture for agentic AI and analyzes OpenClaw plus Ollama as a full-stack system, with Ollama as the LLM inference layer and OpenClaw orchestrating agent runtime. Experiments show persistent memory, tool use, and adaptive decision-making emerge from system-level integration, while the authors discuss scalability, security, privacy, governance, and evaluation challenges.
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[Submitted on 16 Apr 2026]
Title:OpenClaw and Ollama in Agentic AI: Toward Fully Autonomous and Scalable AI Agent Systems
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Abstract: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.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.28629 [cs.AI]
(or arXiv:2607.28629v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.28629
arXiv-issued DOI via DataCite
Submission history
From: Konstantinos I. Roumeliotis [view email] [v1] Thu, 16 Apr 2026 00:28:35 UTC (1,152 KB)
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