Jarvis Nelson Osorio on UC's AI sycophancy study: agreement is a product bug

Jarvis Nelson Osorio argues that finance‑focused AI agents require independent validation to avoid self‑approval errors, emphasizing that “it checked itself” is dangerously insufficient. He cites a University of Cincinnati study showing major models can be coerced into repeating falsehoods—a behavior labeled sycophancy. Additionally, he advocates lightweight agent runtimes over bloated frameworks, stressing explicit authority and cost transparency.