Production LLM agents are overwhelmingly operated as prompted systems — everything the agent must reliably know is restated, and re-billed, on every call. Reflex moves that stable behavior into the weights.
Two compounding structural failures — plus three operational barriers that keep teams locked out of fine-tuning.
Stable prompt segments — tool schemas, formatting rules, few-shot exemplars — are re-transmitted and re-billed at input-token prices on every API call. The cost of behavioral reliability scales linearly with traffic volume.
Even frontier function-calling agents succeed on under half of realistic tool-use tasks, with high inconsistency across identical trials. Beyond a threshold, more prompt engineering stops converting into format compliance.
Any behavior that is stable, frequent, and verifiable should migrate from the context window into the weights — behind a strict evaluation gate.
Four quantitative success criteria across design-partner deployments and benchmark validation. An open-source release of the SDK, training recipe, and evaluation gate accompanies validation.
The thesis is only as good as its enforcement mechanism.