Four Levels of LLM Use: When to Stop at Chatbot and When to Build an Agent

A software developer who has built multiple LLM-based systems — including a document retrieval tool, a text-to-3D pipeline, and a research assistant — outlines a four-level framework for deciding how deeply to integrate large language models into a product. The key variables are how much control is handed to the model and who manages the execution loop. Drawing on Anthropic's December 2024 engineering guidance, the framework distinguishes workflows, where all execution paths are predefined in code, from true agents, where the model dynamically determines its own next steps at runtime. The author cautions that moving to deeper levels is not an upgrade — it trades predictability and error-checking for flexibility. Developers are advised to use the simplest level that meets their requirements, reserving autonomous agents only for tasks too complex to map out as a procedure in advance.