A new paper proposes a layered architecture separating inference, orchestration, and execution for autonomous agents. It utilizes Ollama for the inference layer and OpenClaw for execution. This framework moves beyond reactive LLMs toward persistent, goal-driven systems. Practitioners gain a blueprint for building full-stack agentic workflows with integrated memory and planning.