Simple agent workflows may beat fully autonomous ones in real products
After a year of building and testing product workflows, narrow systems with clear success rules worked better than fully . The strongest setups kept the task small, made success easy to judge, used as few agent loops as possible, returned , and added human approval at important points.
Fully experiments often looked impressive in demos but became costly, hard to predict, and hard to maintain in production. A simple flow with retrieval, one call, a validation layer, and only when confidence was low often performed better than more complex agent designs.
Key points
- Fully can become expensive and unpredictable in production.
- Narrow tasks with clear success rules were easier to run well.
- Reducing agent loops can reduce model calls, token use, and failure points.
- and a validation layer make results easier to check.
- only for low-confidence cases can balance automation and control.