A Simple Definition of What Is, and What Is Not, an Agentic Application

Agentic applications, which leverage Large Language Models (LLMs), are gaining traction as organizations explore new ways to utilize generative AI for business process optimization. These applications, often built by companies or adopted from third-party sources, are defined by their use of LLMs, although some debate exists over whether autonomy and agency should be the defining characteristics. Historically researched as AI agents, these systems now exhibit expanded capabilities due to LLMs, allowing them to develop hypotheses, create content, and learn iteratively. Modern agentic AI systems are increasingly modeled on human cognition, comprising multi-agent architectures with specific roles and coordinated goals, referred to as "cognitive designs." Despite their potential, a common challenge for developing agentic applications lies in building a robust intelligence infrastructure to provide real-time customer intelligence, essential for overcoming the "cold start problem." Snowplow Signals offers a solution by providing real-time customer intelligence infrastructure that integrates with existing AI and ML stacks, enabling sub-second access to user data and facilitating the development of advanced customer-facing agentic applications.