If your autonomous agent made a costly decision tomorrow, could you explain why? As these systems move from lab experiments to enterprise-scale deployment, trust becomes a make-or-break factor. How do you explain decisions made by agents that operate with a high degree of autonomy and ensure they align with business goals, compliance needs, and user expectations? In this panel, engineering and technical strategy leaders share how they’re embedding transparency into agentic AI architectures, from design through to live production environments. Expect candid discussion on the trade-offs between speed, performance, and explainability and what it really takes to win stakeholder confidence at scale. Key takeaways: → Architecting for explainability without sacrificing performance. → Tooling and frameworks for monitoring autonomous decision-making. → Techniques for surfacing model reasoning to non-technical stakeholders. → Balancing governance requirements with innovation velocity.