Over the past few decades, autonomous agents have undergone tremendous evolution, moving from rule
based systems to highly adaptive, learning-driven architectures. These autonomously perceivable, reasoning, and acting
agents have found use in robotics, healthcare, finance, and other fields. This survey provides a comprehensive overview
of the evolution of autonomous agents, highlighting key technological advancements, emerging trends, and persistent
challenges. We explore the role of deep reinforcement learning, multi-agent systems, neuro symbolic AI, and edge
computing in enhancing agent autonomy. Additionally, we discuss critical challenges such as generalization, safety,
scalability, and ethical considerations. Finally, we outline future research directions, emphasizing the need for robust
generalization techniques, improved human-agent collaboration, and the integration of quantum computing and self
supervised learning. This study acts as an important tool for researchers and practitioners aiming to
comprehend the present scenario and prospective of autonomous agents.
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