Cognitive AI agents represent an emerging paradigm in artificial intelligence that combines large language models (LLMs) with reasoning, planning, memory, tool use, perception, and feedback to accomplish complex goals with increasing levels of autonomy. Unlike conventional task-specific AI systems, modern agentic systems can interpret high-level objectives, decompose them into subtasks, reason over available information, construct and revise plans, interact with external tools and environments, and adapt their actions according to observed outcomes. Recent advances in LLM-based reasoning, retrieval-augmented generation, tool-augmented agents, reflective reasoning, multimodal interaction, and multi-agent collaboration have significantly expanded the capabilities of autonomous AI systems. This survey presents a structured analysis of cognitive AI agents with particular emphasis on the transition from reasoning and planning to autonomous action. It examines the major architectural components and cognitive capabilities of modern agents, including goal interpretation, reasoning, task decomposition, adaptive planning, memory, tool selection, environmental interaction, reflection, and feedback. The review further explores the development of single-agent and multiagent systems and their applications in autonomous systems, healthcare, recommender systems, robotics, energy, and other complex decision-making environments. The analysis shows that reliable autonomy cannot be achieved through reasoning or planning alone. Instead, effective agents require a continuous interaction between reasoning, planning, memory, tool use, action, observation, and replanning. Despite rapid progress, important challenges remain in long-horizon planning, memory reliability, tool safety, multi-agent coordination, evaluation, security, explainability, and human oversight.

Nasini PA, Ms.Riya K Prakash, Dr.L C Manikandan, "Cognitive AI agents: From Reasoning and Planning to Autonomous Action" International Journal of Scientific Research and Engineering Development, V9(5): Page(456-467) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.