Agents Simplified: What we mean in the context of AI

AI agents, a concept existing long before the advent of today's advanced large language models (LLMs), have recently gained attention due to the sophistication of modern generative models. These agents, either semi- or fully-autonomous, leverage LLMs as their "brains" to make critical decisions and solve complex tasks by interacting with various tools such as web search engines, databases, and APIs. Historical milestones, like the MRKL Systems and ReAct papers, have shaped the current capabilities of AI agents, emphasizing the importance of external knowledge bases and prompting techniques that enhance reasoning and action. AI agents today comprise core components such as LLMs, tool access, memory systems, and reasoning abilities, allowing them to operate autonomously or in conjunction with human intervention. The future of AI agents includes challenges like ensuring ethical decision-making and advances like multi-modal capabilities and enhanced use of vector databases. Practical applications are already visible in areas like customer service, marketing, and code assistance, showcasing the transformative potential of AI agents in automating tasks and enhancing user experiences.