Operations | Monitoring | ITSM | DevOps | Cloud

Ep 24: Governing AI in the age of agentic systems and Model Context Protocol

On this episode of Masters of Data, we unpack David's new white paper on AI governance for agentic systems. He explains model context protocol (MCP) as "APIs for agents", how AI systems talk and execute tasks. The catch? Autonomous agents are insider threats that move fast and cause serious damage. David introduces the Model Control Plane (MoCop), a twelve-pillar framework designed to prevent your AI from going rogue. We cover his roadmap for security leaders to build real controls and telemetry. His advice: treat agents like interns with root access. Get ahead of this before your agents do.

Automating BGP Troubleshooting with Kentik AI Advisor

In this demo, we use Kentik AI Advisor to troubleshoot a real-world BGP misconfiguration that brings down a peering session with a transit provider. You’ll see how AI Advisor works both as a dedicated page and as an in-portal overlay, using natural language to identify the affected interface, correlate SNMP and syslog data, and pinpoint a maximum-prefix issue as the root cause. Then we accelerate and standardize the workflow with custom network context and AI-powered runbooks, so every engineer can troubleshoot BGP alerts like an expert.

Leveraging AI Crypto Trading Platforms for Smarter Investment Strategies

The world of cryptocurrency has experienced explosive growth over the past decade, transforming from a niche digital asset market into a global financial phenomenon. With this rapid expansion comes a new set of challenges for investors, including high market volatility, an overwhelming number of trading options, and the constant demand for real-time data analysis. Traditional trading strategies often struggle to keep up, leading to missed opportunities and heightened risks. To address these challenges, investors are increasingly turning to technology-driven solutions, most notably, AI crypto trading platforms.

How Agentic AI for ITOps Unlocks Value at Scale

Here’s a paradox for the AI era: organizations are obsessed with the promise of AI as the key to unlocking productivity and enterprise transformation, and IT teams are all-in on the advantages AI and automation offer — yet those same organizations are the ones holding that transformation back. While the majority of IT workers advocate for AI adoption, operational, cultural and budgetary barriers stand in the way of enterprises implementing AI at scale.

The Context Engineering Framework: 3 Shifts for AI-Powered Dev Teams

You’ve probably used AI earlier today. Maybe you asked it to debug a function, generate a test case, or explain a legacy codebase you just inherited. But here’s the thing: you didn’t just type a question and get an answer. You explained your problem, shared background context, pasted code snippets, clarified what you meant, then refined the output until it was actually useful. In other words, you were context engineering.

From Zero Tickets to High-ROI: AI + DEX in 2026 (w/ Samuele Gantner and Vedant Sampath)

Kicking off 2026, Tim and Tom welcome Nexthink Chief Product Officer Samuele Gantner and first-time guest CTO Vedant Sampath for a candid “three pillars” deep-dive on enterprise AI. They explore how AI is reshaping product and engineering: new tooling, new development cycles, and the shift from deterministic software to probabilistic agents—plus the critical role of evals, benchmarks, guardrails, and performance. Then they unpack Nexthink’s three-pillar framework.

From Market Noise to Clear Strategy: How AI Is Changing Business Intelligence

Modern businesses are drowning in data. Every click, transaction, customer interaction, and campaign generates information. Yet having more data does not automatically lead to better decisions. In fact, many organizations struggle because they are surrounded by insights but lack clarity. Reports contradict each other, dashboards multiply, and teams spend more time interpreting data than acting on it. This gap between data and direction is where artificial intelligence is reshaping business intelligence.

From Promise to Practice: What Real AI SRE Can Actually Do When Production Breaks

We’ve written before about the advantages of training an AI SRE on real telemetry data rather than generic Kubernetes documentation. We’ve explained why RAG augmentation based on actual high-scale workload patterns produces better results than LLMs trained on generic scenarios or forum threads. The theory makes sense, the architecture is sound, and the approach is defensible.