In this episode of AI Explained, we are joined by Jeff Malnick, VP of Engineering for Developer and AI Products at 1Password. Jeff brings deep expertise in distributed systems, secrets management, and security infrastructure from his work at 1Password and previously HashiCorp, with a focus on making the secure path the default for developers and now AI agents.

He explains why agent identity has suddenly become urgent: AI agents are machine workloads with reasoning capabilities, which breaks the assumption behind traditional OAuth flows where permissions only need to be granted once. He walks through 1Password's framework of three agent identity models (delegated, bounded, and fully autonomous), why the laptop is the hardest environment to secure now that file system access effectively hands an unlocked machine to a reasoning stranger, and why bearer tokens and passwords are the wrong primitive for agents. He also shares how policy decision and enforcement points need to evolve to pull human intent through to just-in-time authorization, why credentials should never enter LLM context, and where federated versus distributed identity is heading over the next three to five years.

In this episode of AI Explained, we are joined by Stacey Johnston, MD, Chief Information and Digital Execution Officer at Beacon Health System.

Dr. Johnston brings a rare dual perspective to healthcare AI. She trained as a physician and hospitalist before moving into health informatics, giving her firsthand insight into where technology helps clinicians and where it gets in the way. She discusses how Beacon built its AI governance council from scratch, the policies and vendor risk assessments that gate every new tool, and why requiring a defined ROI before approval has become a forcing function for disciplined adoption. She also shares how agentic AI is already delivering results in scheduling, autonomous benefits verification, and colon cancer screening, why ambient listening drove $10,000 in additional per-physician revenue over 12 months, how clinician trust is earned incrementally through seamless workflow fit and real time savings, and what a federated but centrally monitored AI model could look like as health systems scale.

In this episode of AI Explained, we are joined by Jeff Dalton, Head of AI and Chief Scientist at Valence.

Jeff has spent two decades at the intersection of research and industry, from building early conversational search benchmarks at Carnegie Mellon and Microsoft to leading the AI behind Nadia, Valence's purpose-built enterprise coaching assistant. He discusses the fundamentals of agentic system design that still hold from classical AI theory, why evaluation has to come before the prompt, how he approaches memory as a first-class object in coaching systems, and the defense-in-depth approach to guardrails that keeps complex agents safe across diverse enterprise deployments.