An AI agent is a computational entity designed to perform tasks autonomously by interacting with its environment. These agents are capable of perceiving their surroundings, processing information, and making decisions to achieve specific goals. They are used in various fields, from virtual assistants that simulate human conversation to complex systems in materials science that autonomously generate knowledge Mekni, 2021 Oliveira, 2023. AI agents can be embedded in virtual environments to simulate behaviors, such as in crowd simulations for emergency response training, where they model different roles and interactions Sharma, 2019.
AI agents leverage advanced technologies like deep reinforcement learning and large language models to enhance their decision-making capabilities. For instance, in subsurface flow optimization, AI agents use deep learning to provide optimized development plans, outperforming traditional algorithms Nasir, 2021. In clinical settings, AI agents can coordinate specialized tools to assist in decision-making, demonstrating high accuracy in interpreting medical data and providing patient-specific recommendations Ferber, 2024. These agents are designed to be flexible, learning from experience and adapting to new information, which is crucial for their effectiveness in dynamic environments.
The development of AI agents involves integrating various components such as planning, memory, and tool use, which are essential for their functionality. Large language model-based agents, for example, have shown significant advantages in handling natural language and reasoning, making them suitable for complex tasks that require understanding and generating human-like responses Zhao, 2023. The combination of AI and human elements, as seen in AI coaches for sales agents, can enhance performance by balancing data-driven insights with human interpersonal skills Luo, 2020.
In summary, AI agents are autonomous computational entities that interact with their environment to perform tasks, leveraging advanced technologies for decision-making and adaptability. They are applied across various domains, demonstrating flexibility and learning capabilities essential for dynamic and complex environments.
