Hubert Misztela

Agentic Enterprise - What your CEO must know about AI - Hubert Misztela

Jun 3, 2025 · 28:04

Hubert Misztela, an AI research lead at Novartis, argues that organizations may be run by AI agents within three years and must pivot from traditional roles to persona-based workflows to harness agentic automation. He explains that AI agents glue multiple cognitive steps, automating workflows where only humans could operate. Understanding deep context around each workflow is crucial, as this knowledge is often undocumented. Misztela introduces five employee personas (silent achiever, individual contributor, connector, multiplier, knowledge hub) to project how agents merge tasks, magnify impact, and replace roles. He warns that intelligence and domain knowledge become cheap commodities, so companies need multidisciplinary or deeper specialization, and new roles like workflow miner will emerge. Employees must build their own agents using no-code tools, and ethical questions around value alignment remain.

LLM Scientific Reasoning: How to Make AI Capable of Nobel Prize Discoveries: Hubert Misztela

Sep 23, 2024 · 20:00

Hubert Misztela, an AI researcher at Novartis, argues that LLMs require more than naive RAG to achieve scientific reasoning capable of Nobel-level discoveries. He uses the 1990s petunia flower experiment — where three separate biological phenomena (including RNA interference) went unexplained for eight years until their common cause was found — as a benchmark. Misztela classifies question complexity from one-to-one to multi-needle problems and demonstrates that reasoning before retrieval (e.g., routing, GraphRAG) and after retrieval (e.g., relevance classifiers) improves hypothesis generation. His experiments show that strict prompting and a relevance classifier that evaluates each paper's contribution to advancing a hypothesis can recover the correct RNA/DNA link without post-discovery knowledge. He concludes that harder problems demand reasoning steps and that brute-force checking with LLMs may outperform embedding distances.