Fine-tuning forgets. RAG leaks context. Hypernetworks build the model your agent needs on demand.

The article argues that many enterprise AI agent pilots fail because models become less reliable as tasks grow longer and context accumulates. It says fine-tuning can cause models to forget prior knowledge, while retrieval-based approaches can overload or leak context, keeping humans involved in supervision.

The piece points to a shift away from simply orchestrating agents toward improving how models access company-specific knowledge. Hypernetworks are framed as a possible next layer: systems that generate task-specific models on demand, potentially reducing context bloat and model sprawl.