Most developers reach for agent frameworks like Crew AI or Lang Graph when building LLM applications, but this article argues that simple workflows solve most use cases better. The author contends that autonomous agents are unnecessary overhead for problems where the solution path is already known.

The piece breaks down four essential workflow components: control flow, role instructions, prompt builders, and structured output. Unlike agents that dynamically decide their next steps, workflows use deterministic control flow where code owns the execution path. This approach provides transparency and modularity while reducing uncertainty in production systems.

Using the Iris dataset as a case study, the author demonstrates a data-quality investigation workflow built entirely in plain Python without any agentic framework. The workflow flags suspicious records, then uses LLMs to diagnose and explain anomalies with evidence collection.

The author acknowledges frameworks have value for open-ended problems, but for most real-world applications, starting with a simple workflow saves development time and produces more reliable results. Boring, predictable code often beats flashy autonomous agents when delivering production solutions.