General Agent Harnesses Fall Short Against Long-Horizon RL Models

While general agent harnesses are useful for unseen distributions and rapid model switching, Ruslan argues they will ultimately lose to models trained with long-horizon reinforcement learning, as external scaffolding is not a viable long-term substitute.

2026-07-27 ~ 2026-07-27 · 2 related posts

• Generic agent harnesses will not stay ahead of long-horizon RL-trained models, argues Ruslan — ruslansv · 2026-07-27
• Generic harnesses still help on unseen org-specific cases, but not as a long-term substitute — ruslansv · 2026-07-27