This source offers a comprehensive overview of self-evolving agents, illustrating their progression from Large Language Models (LLMs) towards Artificial Super Intelligence (ASI) by increasing their intelligence and adaptability. It systematically organizes the discussion around what, when, and how these agents evolve, examining components like models, memory, tools, and system architecture, along with temporal aspects like intra-test-time and inter-test-time evolution. The text further categorizes evolutionary methods, including reward-based, imitation learning, and population-based approaches. Finally, it addresses the evaluation of self-evolving agents, detailing key metrics such as adaptivity, retention, generalization, efficiency, and safety, while also exploring future research directions in personalization, generalization, safety, and multi-agent ecosystems.