Hypernetworks offer new AI agent autonomy path

Enterprise AI agents often stall in production due to context limitations and 'catastrophic forgetting' when fine-tuned. Traditional solutions like fine-tuning or retrieval-augmented generation (RAG) require human oversight, limiting autonomy. A promising third approach involves hypernetworks, which generate small, task-specific models on demand at inference time. This method bypasses the costs of retraining and the context limits of prompting, potentially enabling agents to run longer, autonomous tasks. Companies like Nace.AI are pioneering this technology, aiming for a 90/10 split between AI work and human validation, though challenges in calibration and scaling remain.