OpenClaw and Ollama in Agentic AI: Toward Fully Autonomous and Scalable AI Agent Systems
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 ...
| Authors | Konstantinos I. Roumeliotis, Ranjan Sapkota |
|---|---|
| Primary category | cs.AI |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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