How to Apply Agentic Meaning to LLM Apps

An LLM (Large Language Model) app is considered "agentic" when it can make runtime decisions that influence the application's control flow, such as selecting tools, planning steps, or handling situations where it cannot proceed safely. Unlike simple chatbots restricted to fixed request-response patterns, agentic systems require the model to actively choose actions, affect execution paths, and interact with tools within predefined boundaries like allowed actions and budget limits. This approach necessitates a different engineering process, emphasizing the importance of clear implementation guidelines, robust evaluation metrics, and traceability to monitor and refine the decision-making process. The text illustrates the concept through examples like support triage agents, research assistants, and code-review agents, each demonstrating how agentic workflows can enhance functionality while ensuring safety and compliance. Additionally, it underscores common pitfalls such as equating agentic with autonomy and stresses the need for rigorous evaluation and observability in development to ensure reliable and efficient outcomes.