Roland Boulos of UnifyApps joins Scott Hebner of theCUBE Research to discuss production-grade agentic artificial intelligence, advancing beyond chatbots that rely solely on large language models toward accountable digital workers. Boulos explains the role of knowledge graphs, persistent memory, connectivity and governance in creating agentic AI. They examine architecture, real-world supply chain use cases and platform strategies for scaling.Key takeaways emphasize treating generative AI as a strategic transformation and building an AI operating system layer that unifies data, context and actions. Boulos states trusted digital workers require grounded enterprise knowledge, multi-type memory and strict governance. Hebner highlights that trust is the currency of enterprise AI and recommends an assembly-first value-driven approach to deliver measurable return on investment.This conversation provides practical insights for enterprise technology leaders and solution architects on data architecture, governance and deployment strategies to scale agentic AI across supply chains and other mission-critical domains.

This episode, "The AI Velocity Trap: Why 85% of Enterprises Stall," examines artificial intelligence adoption and explains why most enterprise initiatives stall despite heavy investment. Nitesh Bansal of R Systems, chief executive officer and managing director, joins Scott Hebner of theCUBE Research to discuss practical approaches that accelerate deployment in complex enterprise environments. Bansal draws on decades of systems and product engineering experience, unpacks the concept of engineering velocity, contrasts model-centric thinking with execution architectures and explores layered approaches such as connectors, governance, evaluations, financial operations and agent templates that accelerate AI deployment in brownfield enterprise environments. They emphasize that engineering velocity rather than model selection determines whether AI scales.Key takeaways include setting clear return on investment objectives, reimagining value chains with an AI-first lens and designing human-in-the-loop workflows. theCUBE Research underscores that governance and trust are necessary but insufficient for scale.Action points include building reusable governance and connector libraries, adopting evaluation-first and FinOps practices and prioritizing high-impact use cases for brownfield environments.

Magnus Revang of Openstream appears on the Frontiers of AI podcast hosted by Scott Hebner of theCUBE Research to discuss transparent trustworthy multiagent artificial intelligence and architectures that move beyond large language model black boxes. Revang explains how specialized plan-based multiagent systems deliver explainability, provenance and operational reliability for high-stakes enterprise workflows. They outline Openstream's multimodal agent approach, which combines symbolic reasoning and knowledge graphs with large language models to support event-triggered high-control deployments and contrast them with low-control prompt-driven copilot scenarios.Revang emphasizes architectural trust controls such as data provenance, explainability, human-in-the-loop collaboration and policy alignment as prerequisites for operationalizing agentic AI. They describe how integrating LLM fluency with symbolic AI, knowledge graphs and specialized agents reduces hallucination and increases determinism. Hebner and theCUBE Research cite survey results showing enterprise leaders prioritize governance and trust as strategic investments.Listen to the full conversation to explore architecture patterns, governance frameworks and deployment strategies for enterprise agentic AI and decision intelligence.

In this episode Scott Hebner of theCUBE Research and Christophe Bertrand of SiliconANGLE and theCUBE present four predictions for enterprise artificial intelligence in 2026. Hebner emphasizes agentic decision intelligence and the need for semantic and causal layers to ensure defensibility and to reshape enterprise architectures; they address implications for explainability and decision systems. Bertrand stresses that cyber resiliency and data governance are prerequisites for large-scale AI deployment and become focal points for attackers and backup strategies; they call for tighter integration across security, storage and data services.The conversation examines agentic architectures, semantic and causal layers, explainability, data management and backup and recovery gaps, and considers evolving vendor dynamics such as DeepSeek-R1 and other platforms. Key implications include stronger governance and compliance requirements, integrated security and storage strategies, improved data protection and operational approaches to minimize risk while enabling innovation in enterprise AI.Listeners gain practical insight into architecture design, governance frameworks and cyber resiliency strategies for 2026 deployments. The episode highlights factors to consider for deploying agentic systems at scale, the importance of defensible models and explainability, and the need for comprehensive backup and recovery planning.

In this insightful episode, we delve into the intricate world of AI agent development with George Gilbert, principal analyst for data and AI at SiliconANGLE and theCUBE. The discussion centers on constructing AI systems that enterprises can rely on, audit, and verify by building on existing large language model frameworks with additional semantic and causal layers.George Gilbert, a distinguished expert in the field, joins Scott Hebner, principal analyst for AI at theCUBE Research, to explore the necessity of developing AI architectures beyond large language models. They discuss the challenges of ensuring AI decisions are logical, accurate, and explainable, and the imperative shift from mere trust to verifiable decision logic anchored in semantic knowledge.The episode outlines the limitations of large language model-based agents, highlighting studies from prestigious universities that demonstrate how such models can mislead with seemingly logical but ultimately unfaithful explanations. Gilbert underscores the vital role of semantic layers and knowledge graphs as the foundation for reliable agentic AI, which allows systems to understand and interact in a meaningful context while causal layers provide solid grounds for defensible decision-making.As the conversation progresses, Gilbert and Hebner emphasize key takeaways such as the importance of semantic architecture for trustworthy AI systems. They stress that building AI agents requires more than fluency; it demands a robust structure that assures enterprise stakeholders of the systems' reliability and accountability. This episode is essential for those seeking to innovate responsibly in AI.

Join us for an insightful episode from the Next Frontiers of AI Podcast, hosted by Scott Hebner, principal analyst for artificial intelligence at theCUBE Research. This episode explores the impending rise of AI decision intelligence and its anticipated mainstream adoption in 2026. Our discussion features an expert perspective from Joel Sherlock, CEO of Causify, focusing on revolutionary changes in AI decision-making technologies.In this engaging discussion, Sherlock shares their journey and expertise in causal modeling. As the CEO of Causify, Sherlock provides unique insights into how causal decision intelligence can transform enterprise decision-making. The episode, facilitated by Hebner and the theCUBE Research team, examines critical market trends, including the shift from generative AI to agentic workflows in businesses.Key takeaways from this episode include the impact of AI decision intelligence on trust and reliability in AI-driven decisions. Sherlock discusses how causal AI is essential for creating explainable and auditable decision-making processes, a notion echoed by analysts at theCUBE Research. The dialogue underscores the necessity of a robust AI architecture beyond large language models to make AI truly decision-grade.

Join us for an insightful discussion as Scott Hebner of theCUBE Research engages with Ben Currin, CEO of Vantaca. They explore the transformative role of agentic AI in reshaping software as a service (SaaS) models, drawing lessons from Vantaca's ascent to unicorn status. This video is part of an ongoing series on the evolution of digital labor in modern service businesses.In this episode, Ben Currin shares their journey from a nuclear engineer to leading Vantaca, a pioneering platform company in the community association management space. They discuss the challenges and opportunities in managing vast ecosystems, highlighting Vantaca's innovative use of agentic AI. Hosted by Hebner, the conversation delves into the nuances of digital labor transformation and Vantaca’s strategic milestones.Key insights from the discussion include the pivotal role of agentic AI in driving business growth, as stated by Currin. They emphasize the necessity of reimagining work processes with cross-organizational collaboration for substantial return on investment. According to Hebner, the focus on knowledge-centered AI work is critical in providing Vantaca a competitive edge, setting a benchmark in the industry.