The next generation of AI-powered commerce is moving beyond recommendation engines toward intelligent reasoning. In this episode of AppDevANGLE, Paul Nashawaty speaks with Konstantin Kiselev, CTO and Co-Founder of Haut.AI, about how enterprises are using causal reasoning, knowledge graphs, computer vision, and clinical validation to build trusted AI experiences that go far beyond traditional product recommendations. The discussion explores why statistical correlation alone is insufficient for high-consequence decisions, how domain-specific reasoning improves consumer trust, and why explainable AI is becoming a strategic requirement for enterprise applications in healthcare, beauty, and personalized commerce. As organizations increasingly adopt agentic AI, the ability to combine structured knowledge with real-world validation may become a key differentiator for next-generation customer experiences. Key Highlights: The evolution from recommendation engines to reasoning-based AI Why explainable AI is critical for personalized commerce How causal knowledge graphs improve AI decision-making Clinical validation as a foundation for trusted enterprise AI Continuous feedback loops and adaptive AI recommendations The importance of domain intelligence in agentic AI systems Building AI applications that balance personalization with trust Enterprise implications for the future of agentic commerce

Artificial intelligence is transforming software engineering—but enterprise security practices are struggling to keep pace. In this AppDevANGLE conversation, Paul Nashawaty speaks with Brian Fox, Co-Founder and CTO of Sonatype, about how AI-generated code is changing software supply chains and why organizations must rethink application security for the AI era. Brian explains why traditional security reviews cannot scale with AI-assisted development, how grounding large language models with real-time enterprise data dramatically improves security outcomes, and why dependency management is becoming a foundational capability for AI-native software engineering. The discussion also explores vulnerability debt, open source governance, SBOMs, AI-driven security automation, and the future of secure software delivery. As enterprises accelerate AI adoption across the software development lifecycle, the competitive advantage will belong to organizations that combine developer productivity with automated, policy-driven security. Key Highlights: AI-generated code and the rise of software supply chain security Why AI models require real-time security and dependency intelligence Hallucinations vs. hesitation: emerging risks in AI-assisted development Managing vulnerability debt in AI-native software engineering Grounding AI with enterprise context using live security data Why software dependency governance must become machine-readable AI-powered security automation for modern DevSecOps Enterprise strategies for secure AI-driven application development 00:00 - Intro 00:09 - Introducing AI Leadership and Challenges in Software Development 02:20 - Effective Security in AI Development 04:38 - AI Dynamics: From Real-Time Insight to Decision Dilemmas 08:53 - AI-Driven Development and Security 12:57 - Future Insights and Closing Thoughts on AI in Software Development

The next phase of enterprise AI adoption will be defined less by model innovation and more by data readiness. In this AppDevANGLE conversation, Sam Newnam, VP of AI Solutions & Business Development at Hammerspace, joins Paul Nashawaty to discuss why data has become the primary constraint preventing organizations from realizing AI value at scale. As enterprises move from pilot projects to production deployments, many are discovering that fragmented data environments, governance challenges, and operational complexity create far greater barriers than model availability or infrastructure capacity. The discussion explores data gravity, metadata management, security requirements, hybrid cloud environments, and the growing need for AI-ready data platforms that can simplify enterprise operations. For technology leaders, platform teams, and AI practitioners, this conversation provides practical insights into building a scalable foundation for enterprise AI. Key Highlights Why enterprise AI has become a data readiness challenge The impact of data fragmentation on AI deployment success Understanding data gravity in modern AI environments Why governance and security must travel with enterprise data The role of metadata in AI operationalization Reducing complexity across hybrid and multi-cloud environments How organizations can improve AI ROI through data strategy Why integrated AI data platforms are replacing siloed toolchains Practical steps for moving from AI experimentation to production at scale 00:00 - Intro 00:07 - Navigating AI: Challenges, Players, and Innovations 03:27 - Optimizing AI: Balancing Constraints and Resources 06:42 - Costs of Data Preparation 08:52 - Enhancing Skills with Intelligent and Secure Data Solutions 12:16 - Optimizing Success: Addressing AI Project Challenges and Data Pipeline Alignment 15:07 - AI ROI and Project-based Approaches 17:26 - Concluding Reflections

Enterprise data infrastructure is entering a new phase. As organizations expand Kubernetes adoption and explore AI-driven operations, platform teams are rethinking how databases are deployed, managed, and modernized at scale. In this AppDevANGLE conversation, Peter Farkas, CEO of Percona, joins Paul Nashawaty to discuss the growing maturity of stateful workloads on Kubernetes, the operational realities of AI-assisted database management, and why open source remains central to enterprise platform strategy. The discussion explores how Kubernetes operators are reducing operational complexity, where AI can improve observability and diagnostics, and why enterprises increasingly value flexibility and portability over proprietary lock-in. For IT leaders, platform engineers, and application development teams, this conversation offers practical insight into the future of cloud-native database operations. #theCUBEResearch #Kubernetes #PlatformEngineering #OpenSource #DatabaseOperations #CloudNative #EnterpriseAI #AIOps #DevOps #DigitalTransformation Key Highlights The evolution of databases on Kubernetes from risk to operational best practice How Kubernetes operators are transforming Day 2 database management Why not every database workload belongs on Kubernetes The real-world impact of AI on observability and diagnostics Why autonomous databases remain more vision than reality The rise of outcome-focused database operations Open source usability as the next competitive battleground Why infrastructure freedom and portability are strategic priorities for enterprises How Kubernetes and AI are reshaping modern data platform architectures 00:00 - Intro 00:07 - Kubernetes and Databases: Insights with Percona's Leadership 02:10 - Title: "AI-Driven Database Management: A Pragmatic Shift to Kubernetes 05:44 - Challenges of Running Databases on Kubernetes 09:07 - Title: "Navigating AI in Database Management: Potential, Limitations, and Augmented Approaches 12:01 - Percona's Evolution: Advancing Database Management and Embracing Kubernetes 14:07 - Title: "Embracing Freedom: Strategies for a Future-Proof Technological Landscape