Why Generic AI Agents Don’t Work In Regulated Industries

InnovationWhy Generic AI Agents Don’t Work In Regulated IndustriesByArun Ramakrishnan,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based)Jun 01, 2026, 12:00pm EDT​Arun Ramakrishnan is co-founder and CTO of LogicFlo AI. gettyNot long ago, when people interacted with AI systems, they were experiencing them as chatbots. You asked a question, got an answer, maybe asked a follow-up and that was the end of the interaction. Today, we’re seeing more and more AI agents. Everybody’s building them. Coding agents. Research agents. Writing agents. Agents that can browse, summarize, plan, retrieve information, call tools and then continue looping through tasks on their own. Most conversations are still centered around the model itself. Which model are you using? GPT? Claude? Open-source? What benchmark does it hit?​But the better question is: How are your agents making decisions?​There’s a phrase people use sometimes when talking about large language models: “stochastic parrot.” Agents simply predict likely next outputs based on patterns they’ve seen before. That’s what makes them powerful, but it’s also what makes them dangerous in high-stakes environments. The model itself is not deterministic. It’s generating answers based on probabilities.​Which is fine if you’re asking it to brainstorm headlines or help write code.​It becomes a very different conversation when the system is operating inside highly regulated industries, such as pharma, life sciences, finance or government, where every action may need to be audited later.The Harness Is The Real ProductUsually, when people say “agent,” what they really mean is the AI model in the middle. Think of the model as the agent’s brain. The harnesses are the limbs. They do the work the brain tells them to do. They make the agent trustworthy and auditable inside a high-stakes workflow. ​Broadly,...