Systems | Development | Analytics | API | Testing

Introducing Kong A2A and MCP Metrics: Visibility and Governance for AI Tool Adoption at Scale

Scaling LLM and agentic AI adoption from pilot programs to enterprise-wide deployments is a massive logistical rollout. As AI and agentic usage grow, so does a nagging question for leadership: **Are agents using the right tools to get the job done?** While raw infrastructure metrics might tell you if a server is "up," they fail to tell you if your AI investment is being leveraged.

How Semantic Layers and Ontologies Create Trusted AI

Learn why an organization’s ontology, a structured framework for how a business defines, connects, and makes sense of its data and knowledge, is the most valuable and most overlooked asset in any AI strategy. Jessica Talisman, CEO and Founder of The Ontology Pipeline, and Tony Seale, Founder of The Knowledge Graph Guys, break down what it actually takes to build trusted AI, covering everything from semantic layers and knowledge graphs to why provenance is non-negotiable.

Ontology: The Secret to Semantic Layers | The Data & AI Chief Podcast

Is your AI-driven "autonomous enterprise" a reality or a peak-of-inflated-expectations dream? Most organizations rush toward the end state of AI agents without doing the foundational work of defining how their data actually relates through a robust ontology. In this episode of The Data & AI Chief, we sit down with Tony Seale, Founder of The Knowledge Graph Guys, and Jessica Talisman, CEO and Founder of The Ontology Pipeline. We break down why the "lost art" of data modeling and the development of semantic layers are the secret weapons for scaling Agentic Analytics.

Stop the AI Iceberg | Secure AI Using Ontologies and Semantic Layers

Don’t let the "AI iceberg" sink your IP Most leaders only focus on the flashy models at the surface, but the real value—and the risk—is what’s underneath. Tony Seale and Jessica Talisman reveal why turning AI back onto your own data infrastructure to build connected ontologies is the key to security. This semantic foundation is the core of Agentic Analytics, ensuring your insights are grounded in your specific business logic rather than generic LLM guesses.

Ep 70 | AI Risk & Cybersecurity: Theresa Payton on the New Threat Landscape

As AI adoption accelerates, so do the risks that come with it. So what happens when AI puts cyberattack capabilities into everyone’s hands? In this episode of The AI Forecast, Paul Muller is joined by Theresa Payton to break down the new reality of AI-powered threats. Drawing on decades of experience as the first female White House CIO, CEO of Fortalice Solutions, and the author of four books on privacy and big data, Theresa explains why AI has fundamentally changed the rules of cybersecurity and why most organizations are still playing catch-up.

AI Connection Pooling Best Practices | DreamFactory

Key takeaways: For AI workloads, pooling must handle long connection hold times and heavy traffic. DreamFactory is a secure, self-hosted enterprise data access platform that provides governed API access to any data source, connecting enterprise applications and on-prem LLMs with role-based access and identity passthrough. Combined with tools like PgBouncer, these solutions free connections faster and improve scalability. Simple tweaks, such as segmenting pools and setting timeouts, can boost efficiency.

Why traditional QA metrics fall short as AI enters the pipeline

Take this scenario: Your team ships a release with 91% code coverage. Every test in the suite passes. The pipeline is green, and leadership signs off. But two days later, a critical defect surfaces in production. Upon investigation, you find that the changed code was never actually tested, and the tests that were run covered different paths entirely. That 91% was real, but it was just measuring the wrong thing. And as AI tools generate more of the code inside those pipelines, the gap widens.

On-Prem and Private Cloud Deployment Models for Analytics

Leadership keeps asking for more dashboards, faster answers, and tighter compliance. The data team hears a different message: do more with the same staff (or, fewer). That is where the difficulty evaluating on-prem and private cloud deployment models for corporate data analytics and visualization solutions starts to bite.