The distinction between workflow-based agent pipelines and truly autonomous harness loops is spot on. In practice, having deterministic fallback rails around tool invocation saves so much debugging headache compared to giving an open-ended loop full autonomy.
Evelyn Wyatt
Building AI Systems That Actually Work in Production
I think the distinction between models, workflows, and harnesses is important when building production-ready AI systems. A capable model alone doesn't guarantee reliable execution. The surrounding system needs to manage context, tool permissions, state, error recovery, and verification. One challenge is keeping workflows flexible enough to adapt to unexpected situations without giving agents unrestricted control. A well-designed harness should provide that balance while making execution observable and outcomes verifiable.