AI Explained

In this episode of AI Explained, we are joined by David Kenny, Executive Chairman of the Board of Nielsen and veteran AI leader. 

He discusses how to prevent AI agents from going rogue, sharing insights from Nielsen's "Ask Nielsen" platform and emphasizing the importance of compound AI systems, real-time control planes, "generally accepted trust principles" (GATP) for AI, fit-for-purpose models, production cost management, and change management in moving from pilot to production.

In this episode of AI Explained, we are joined by Gary Stafford, Principal Solutions Architect at AWS Strands Agents.

He delves into how enterprises choose between AI/ML and agentic approaches, patterns for multi-agent systems, and the role of MCP. Gary also shares real-world use cases and practical guidance on safety, scaling, and delivering enterprise-ready agent systems.

In this episode of AI Explained, we are joined by Jayeeta Putatunda, Director of AI Center of Excellence at Fitch Group. 

She discusses essential lessons learned from building and deploying AI agent systems, including challenges in moving from concept to production, key evaluation metrics, and the importance of observability and guardrails in ensuring reliable AI systems.

In this episode of AI Explained, we are joined by Nate B. Jones, AI strategist.

He explores high-level advice for organizations, technical ideas such as prompting and application architecture, and the current state of agent adoption. Key topics include challenges in building production-ready agents, architectural decisions, and ensuring ROI from these agents.

In this episode of AI Explained, we are joined by Karthik Bharathy, General Manager, AI Ops & Governance for Amazon SageMaker AI at AWS. 

He discusses the critical aspects of AI security and observability for agentic workflows. He covers the evolution of AI Ops, end-to-end observability, human oversight, the current state of AI in enterprises, and the ways agentic AI systems are transforming business operations. He also dives into the challenges of implementing AI security, evaluating AI decisions, and ensuring transparency and compliance.