OpenClaw and Ollama: A Full-Stack Architecture for Autonomous AI Agents
A new study released on arXiv (2607.28629) presents a detailed layered model for Agentic AI, emphasizing the transition from reactive large language models to autonomous systems capable of sustained actions. The research highlights notable gaps in differentiating the layers of inference, orchestration, and execution. It assesses OpenClaw and Ollama as an integrated system, where Ollama acts as the LLM inference component and OpenClaw manages agent runtime orchestration, including reasoning, tool use, and action execution. A prototype demonstrates features like persistence and ongoing execution. The study outlines the evolution from reactive LLMs to goal-oriented agents with memory, planning, and continuous execution, aiming to create a unified framework for developing and evaluating comprehensive agentic systems.
Key facts
- Paper arXiv:2607.28629 proposes a layered architecture for Agentic AI.
- OpenClaw and Ollama are analyzed as a full-stack Agentic AI system.
- Ollama serves as the LLM inference layer.
- OpenClaw enables agent runtime orchestration.
- The architecture integrates reasoning, tool use, and action execution.
- Prototype validation demonstrates persistence and continuous execution.
- The paper addresses gaps in separating inference, orchestration, and execution layers.
- The work aims to provide a unified framework for designing and evaluating agentic systems.
Entities
Institutions
- arXiv