Prometheus Native Histograms FTW | Big Tent S3 Ep2
From Grafana Lab's Big Tent podcast - season 3, episode 2. Thanks for watching!
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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.
Another year, another $750,000 to Open Source maintainers
Bored yet? 2025 was the fifth year in a row (2024, 2023, 2022, 2021) that Sentry gave a pretty hefty chunk of change to the maintainers of the Open Source software that we rely on and love. This is our first report since we launched the Open Source Pledge, which brings together companies that share our respect for the independent maintainers in the community. Pledge members have collectively paid $4.5M to Open Source maintainers and foundations since launch. No more excuses!
AIOps Use Cases for IT
Core thesis: AIOps solves the enterprise gap between telemetry volume and operational decisions by converting noisy signals into prioritized incidents with probable causality, so recovery gets faster and repeat failures stop.
From Compliance to Confidence: Earning Trust in a World That Never Stops Changing
Compliance has always been a necessity, but for many organizations, it has also been a burden. Reports, audits, manual reviews, and spreadsheets create a cycle of looking backward rather than moving forward. As systems become more dynamic, that lag between compliance checks and real-world change grows wider, creating risk that traditional methods can’t close. The goal now isn’t to check the box.
Understanding Lighthouse: Has a Viewport Meta Tag
You ran Lighthouse and got a passing audit for “Has a tag with width or initial-scale.” Great. But do you know what happens when it’s missing? Your users wait an extra 300 milliseconds on every single tap. On mobile, that’s an eternity.
5 Observability & AI Trends Making Way for an Autonomous IT Reality in 2026
IT operations are changing faster than most people realize, making autonomous IT a 2026 reality, not a distant vision. Your team monitors tens of thousands of metrics, ingests terabytes of logs, and generates thousands of alerts daily. And somehow, you still find out about outages from customers before you see them in your tools. That gap between having visibility and actually understanding what’s happening has become the central problem.
Agentic AI in SolarWinds Observability Self-Hosted with Demo (2026)
In this video, SolarWinds Tech Evangelist Chrystal Taylor and Staff Technical Trainer Cheryl Nomanson walk through a hands-on demo of the SolarWinds AI Agent, currently available for tech preview in SolarWinds Observability Self-Hosted.
How to Test Network Performance: 8 Testing Methods + Tools (2026 Guide)
Network performance directly impacts business productivity, user experience, and revenue. When applications lag, video calls freeze, or file transfers stall, the root cause often lies in untested network infrastructure. Yet many organizations monitor their networks reactively—only testing performance after problems emerge. This article shows you how to proactively test network performance using proven methodologies that identify issues before they affect users.
A Guide to Regression Analysis with Time Series Data
Regression analysis with time series data in Python provides a basis for understanding how values change over time. By following this guide, you’ll understand regression as applied to time series data, how to prepare it in Python, and how to create regression models that’ll help discover trends and influence decisions. With the vast amount of time series data generated, captured, and consumed daily, how can you make sense of it?