You Probably Don’t Need an Agent Framework

Instead of building complex, autonomous LLM agents, many real-world problems are better solved using a more controlled and transparent workflow approach. In this method, developers define the overall process and use the LLM as a powerful reasoning engine within specific, pre-defined steps. Building an effective workflow involves designing the control flow, creating specific instructions for the LLM at each stage, and demanding structured outputs to ensure reliability. This approach grants developers deterministic control over the application's path, making the system more modular and easier to debug. For example, a simple Python workflow can diagnose data anomalies by first using an LLM to investigate with predefined tools and then having it generate an evidence-backed explanation.