The presentation outlines the evolution of frameworks for end-to-end workflow automation, designed to address the complexities of building, deploying, and maintaining robust edge AI autonomous systems. It explores the integrated approach that automates the entire lifecycle, from data ingestion and model training to deployment on heterogeneous edge devices and continuous operational monitoring. The discussion highlights strategies for addressing the rise of agentic AI in workflow design and automation, particularly with the emergence of agentic edge AI systems that function with a degree of autonomy, understand intent, learn from context, and take initiative without relying on predefined instructions.