arXiv paper maps OpenClaw-Ollama stack for full-stack agentic AI systems

An arXiv cs.AI paper titled OpenClaw and Ollama in Agentic AI: Toward Fully Autonomous and Scalable AI Agent Systems presents a layered architecture for agentic AI that separates inference, orchestration, and execution. The abstract describes a shift from reactive large language model interfaces to persistent, goal-driven agents with memory, planning, and continuous execution. It analyzes OpenClaw and Ollama as a full-stack system in which Ollama serves as the LLM inference layer and OpenClaw provides agent runtime orchestration that integrates reasoning, tool use, and action execution. A prototype experimental validation of the OpenClaw-Ollama architecture is said to show that persistent memory, tool utilization, and adaptive decision-making emerge from system-level integration rather than standalone models, with performance improving as architectural complexity increases. The study also discusses challenges in scalability, security, privacy, governance, and evaluation, and calls for stronger benchmarking and system-level design. Future directions listed include scalable multi-agent architectures, distributed autonomous systems, and human-aware agentic frameworks. The authors state that models, code, and datasets are publicly released to support reproducibility and benchmarking.