Operations | Monitoring | ITSM | DevOps | Cloud

Diff-erent Perspectives: How Specialized LLM Personas Catch More Bugs

We’ve built a multi-LLM PR reviewer that runs on every pull request in a couple of our own repos. Two independent models look at each change in parallel, each wearing a set of “persona hats” tuned to a specific area of the codebase. They compare notes, duplicates get stripped out, and the PR author ends up with a single review comment rather than a wall of noise.

Qovery Q1 2026 Demo Day

See our latest retrospective and live updates. We're showcasing Event-Based Autoscaling via KEDA, allowing you to scale on business metrics that actually matter. We’ll also debut Copilot Troubleshoot to solve complex deployment failures instantly, demonstrate how MCP Agents are setting a new standard for your workflow, and share more about NGINX migration. Qovery is the Kubernetes management platform built for the AI era.

Building the AI Stack for Modern Network Operations - Surya Nimmagadda

AI is rapidly transforming network operations — but what does it actually take to build an AI stack that works in production? In this session from AI for Network Leaders – Powered by Selector, Surya Nimmagadda breaks down how modern AI systems for network operations are designed, deployed, and used today. He covers: This session is designed for network engineers, architects, and operators looking to move beyond theory and understand how AI is being applied in real production environments.

Inside the AI Agents Transforming Network Operations - Joby Rudolph & James Schnebly | Selector

AI agents are becoming a core part of modern network operations — but what does it actually take to build and deploy them effectively? In this session from AI for Network Leaders – Powered by Selector, Joby Rudolph and James Schnebly break down how AI agents are designed, implemented, and applied in real-world network environments. They cover: This session provides a practical look at how AI agents are moving from concept to production — and what it takes to make them work at scale.

Uptrace MCP Server: Auto-Generate Dashboards with AI in Minutes

Tired of clicking through menus to build observability dashboards? In this video I walk through how to configure the Uptrace MCP (Model Context Protocol) server and connect it to an AI assistant so your dashboards get created automatically from natural-language prompts. You'll learn how to: By the end you'll have a working setup where describing what you want to monitor is enough to get a real, shareable dashboard in Uptrace.

AI Meeting Bots Were Just the Beginning. Meet the AI Collaborator

Why the next era of enterprise AI isn’t about note-taking — it’s about digital workers who actually show up and do the work. There’s a moment every IT operations leader knows well. A critical incident hits at 2 PM on a Tuesday. Within minutes, a war room meeting spins up — a Google Meet or Teams call crowded with network engineers, SRE leads, cloud architects, and storage admins, all staring at dashboards and talking over each other. Someone is manually pulling syslog data.

Debug frontend issues with AI: Real user monitoring meets the Coralogix MCP server

It is 2 AM. Someone on-call gets paged. Conversion rates on the checkout page dropped 30 percent in the last hour. The immediate questions are familiar. Is this a JavaScript error? A slow API call? A broken third-party script? A performance regression that never throws an exception but quietly drives users away? In most teams, answering those questions is not hard because the data is missing. It is hard because the investigation is split across too many places.