
Automation, Accountability and Agency with Vyasa Murthy
This episode explores why traditional model risk governance does not fully fit agentic AI, especially when systems can choose their own path and act at machine speed. Anurag is joined by Vyasa Murthy, CEO and founder of Venture Vertex, who explains why banks and regulated organizations need real-time control, not just logs and after-the-fact review.We discuss how agent chains create new accountability gaps, why evidence must be captured at the moment of action, and how runtime governance can become a competitive advantage as AI moves deeper into critical workflows.Key topicsIn this episode, Anurag frames the shift from AI that recommends to AI that acts, and why that changes the governance problem in regulated environments.Vyasa Murthy explains his background in enterprise technology across banking, cards, wealth, and insurance, and how years of seeing controls quietly erode led him to build Venture Vertex and VARC.The conversation contrasts classic model risk management with agentic systems, where the approved behavior is no longer a fixed function but a path chosen at runtime.Vyasa argues that logging alone is not enough because by the time an analyst reviews the record, the damage may already be done.A key example is data loss prevention: a control may start with a legitimate exception, then slowly become effectively disabled for the sake of convenience.The episode shows how chained agents can each pass their own checks while the overall outcome, such as a payment, has no single accountable owner.Anurag and Vyasa connect this to segregation of duties and the human “uneasy feeling” that often acted as an informal guardrail in manual workflows.Vyasa describes VARC as verifiable AI runtime control, designed to intervene at the moment an agent tries to act, not after the fact.The discussion introduces graduated response orchestration, moving beyond simple allow or deny decisions to options like flag, escalate, freeze, or decommission a session.The episode closes on the idea that organizations can outsource components of the AI stack, but not accountability, evidence, or the ability to prove what their agents actually did.













