Agentic AI Workflows: Key Design Flaws and What Engineers Should Build First
A developer series on agentic AI workflows has identified core failure points observed across real production runs. Instructions given to language models act as strong defaults rather than guarantees, meaning any critical rule not backed by a script is likely to be skipped silently. Data contracts between components frequently go untested, causing failures only after code has already shipped, while loop histories get overwritten and concurrency is constrained by shared infrastructure rather than the workflow itself. Measuring genuine improvement also proves difficult, as common metrics like elapsed time and token counts are easily distorted by caching, model changes, or operator familiarity. The author recommends tracking specific recurring frictions and counts of expensive operations — such as environment boots and human wait events — as the most reliable indicators of progress.
