The concept of AI agents is explored through the lens of varying degrees of "agentic" capabilities, which refer to the autonomy and decision-making power granted to systems using Large Language Models (LLMs). While traditional perceptions of AI agents focus on advanced, autonomous, human-like systems, the text proposes a spectrum of agentic behavior, much like the levels of autonomy in autonomous vehicles. This spectrum ranges from simple routing tasks to complex autonomous agents that build and adapt tools for future tasks. The text emphasizes that understanding the degree of agentic behavior can guide the design and development of LLM applications, influencing decisions around frameworks, monitoring, and evaluation. The author argues for the need for new tooling and infrastructure as applications become more agentic, highlighting tools like LangGraph and LangSmith created to support such agentic systems. This nuanced understanding of agentic behavior is positioned as critical for the efficient and robust development of AI applications.