Multi-step AI workflows—where distinct automated steps hand off to each other—are a powerful pattern. However, they introduce a valid concern: Cumulative Error.
If a workflow requires 5 sequential steps, and each performs with 90% accuracy, the overall system reliability is mathematically 59% (0.9^5). This can create operational bottlenecks if not managed.
To de-risk these systems, we recommend Flatter Workflows. Instead of deep chains (Step A -> B -> C -> D), use a central "Coordinator" pattern.
The Coordinator holds the overall context and delegates focused tasks to specialized sub-processes, reviewing the output at each step. This allows for error correction between steps, preventing a small mistake early on from cascading into a major failure.