Four Levels of Using an LLM: From Chat to Agents
I have built several different kinds of LLM systems: a retrieval system that answers questions over internal documents, a pipeline that turns text into 3D models, and a deep research tool I use myself. Same idea — "let the model do the work" — but all three ended up built differently. Two questions decide which one you get. First, how much do you hand to the model? Second, once you have handed it…
The article discusses four distinct levels of using language models (LLMs), ranging from simple chat interfaces to more complex agents that dynamically direct their own processes. The first level involves using the provider's built-in chat interface, like ChatGPT, Claude, or Gemini, for specific tasks. Once you move beyond this, you enter levels 2, 3, and 4, which involve building more sophisticated workflows or agents.
The key differences between these levels lie in how much control is handed to the model and who is responsible for driving the next steps in the process. The article defines an agent as a system where the LLM dynamically directs its own actions and tool usage, as opposed to workflows where the path is predetermined through pre-written code.
The article also addresses common objections to this dichotomy, arguing that autonomy is a spectrum rather than a binary distinction. It explains that while agent code may be simpler than elaborate workflows, the true complexity lies in the model's decision-making process during runtime.
Finally, the article suggests that the decision to transition from a workflow to an agent should be based on four key checks: the complexity of the task, the value of the output, the suitability of the task for LLMs, and the feasibility of adding guardrails to ensure safe automated execution. If the task is too complex, the result is not worth the investment, or it falls outside the LLM's expertise, the article recommends staying with a simpler workflow.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
