A key strength of GenAI is its capacity to adapt to a wide range of applications across diverse use cases with high impact results, with one of the most significant being the way that GenAI has transformed the pre-GenAI wave of AI assistants and chatbots. First wave AI assistants were rule-based and rely on predefined workflows and preprogrammed responses and narrow natural language understanding (NLP). Their capabilities were typically limited to specific tasks like answering FAQs, executing simple commands, or retrieving predefined information. GenAI assistants, powered by sophisticated large language models (LLMs), have improved traditional AI assistants by enabling them to better understand context and generate nuanced, human-like responses, often in near-real time. This makes GenAI assistants more versatile, and allows for more engaging, personalized multi-turn conversations.

Humans set goals for AI agents, but the agent determines how to achieve the goal, proactively performing supportive tasks with little or no human intervention. AI agents can react to changes in their environment (more ably if environments are well defined), responding dynamically and adapting in ways that enable them to effectively complete often complex tasks more effectively than their human counterparts. Unlike traditional AI assistants, AI agents can interact with and make use of external datasets, applications, even other agents and things in the physical world. Compared to a basic query