Operations | Monitoring | ITSM | DevOps | Cloud

Minga's developers point AI tools at their own infrastructure

Matt Zytaruk, VP of Technology at Minga, on what changed once his developers could point their AI tools at their own infrastructure. Minga checks in 1.5 million students across the eastern seaboard every school morning. They run that platform on Control Plane, which gives their team an MCP server, a CLI, and Terraform-native resources — so a developer can investigate an issue and generate the code for the change without waiting on anyone.

Alert fatigue, AI triage, and incidents: Lessons from observability experts at Cyera, PlayHQ & NAB

Observability looks perfect in a slide deck – in practice, it's messier. In this panel, engineering leaders from Cyara, PlayHQ, and National Australia Bank share what really happened when they scaled observability: unexpected cloud bills, alert fatigue, a weekend database outage caught by an AI-assisted triage agent, and a vendor dispute settled by a single chart. They also cover moving beyond legacy tooling, using AI to close the PromQL skills gap, and what's next – from agentic SDLC integration to continuous profiling. Real stories, real numbers, real lessons.

AI speeds up delivery. Here's how IT leaders manage the risk when AI-generated code hits production.

AI can accelerate speed to market, but for IT leaders it also raises a harder question: can you prove how an AI-generated change reached production? Chris Yates (SVP, Managing Director of Data & Architecture, Republic Bank) explains how his team builds a full evidence trail for every change, using version-controlled deployment tooling like Redgate Flyway Enterprise, so governance becomes a guardrail rather than a brake on speed.

Inside the Gartner Market Guide for CSP Service and Network Assurance Solutions: Agentic AI and the Foundation It Runs On

Most CSP assurance roadmaps now carry an AI line item. Fewer have a clear answer for what that AI actually runs on. Over the past year, the working question across operators and vendors has narrowed to something practical: how to put agents to work in assurance while keeping operators in control.

Why AI Adoption Fails Without Operational Maturity First

Most MSPs are already experimenting with AI in some form, and every vendor at every conference has an AI for MSPs pitch ready. Few have stopped to check whether their own operations are solid enough to scale. Our 2026 IT Trends Report found that three-quarters of IT leaders believe they have an AI policy, while fewer than half of help desk staff agree. That’s the real risk: AI doesn’t fix drift, unclear ownership, or gaps between what’s documented and what’s actually happening.