You Probably Don't Need an Agent Framework

For many practical LLM applications, developers might find more success with clear, hand-rolled workflows rather than complex agent frameworks. The article argues that while agent frameworks are powerful for open-ended problems requiring dynamic decision-making, many real-world scenarios benefit from explicitly defined steps. A robust workflow can be built using plain Python, incorporating elements like control flow, specific role instructions, prompt builders, and structured outputs. An example cited involves a data-quality investigation where a Python screening step flags anomalies, an LLM investigator gathers evidence using local tools, and an LLM explainer produces a final assessment with structured outputs. This approach allows for greater control and easier debugging. Agent frameworks are suggested to be more appropriate when transitioning from a prototype to production, or when dealing with highly open-ended problems where solution paths are unpredictable. The core lesson is to prioritize clear workflows and add complexity only when the problem explicitly demands it, as the components of a hand-rolled workflow can often be carried over if an agent or framework becomes necessary later.