Operations | Monitoring | ITSM | DevOps | Cloud

What happens when AI agents stop being scattered and start being steered? Customer service queues shrink, teams get time back for high-value work, and everyone finally works off the same data. That’s the power of the ServiceNow AI Control Tower—all your AI, all under control. No more fragmentation. No more busywork. Just visibility, control, and workflows that scale across the entire business.

In Episode 3 of Elephant in the Room, we move from theory to practice, building a real, community-focused application live on stream. Join Jay Miller (Aiven), Abigail Dogbe, and Andres Pineda as they collaboratively design and build a CFP (Call for Proposals) review platform using PostgreSQL and Django. The goal: create a practical tool that helps speakers get better feedback on their talks, and helps organizers discover new, diverse voices.

Most software intelligence tools take months to implement, cost a fortune, and end up collecting dust. GitKraken Insights is different. It helps engineering leaders measure what matters: AI impact, code quality, delivery performance, and developer experience, all in one place. It’s the latest evolution of the GitKraken DevEx platform, trusted by over 40 million developers. Insights connects data from across your GitKraken tools to give you a complete picture of engineering health and value. We're talking DORA metrics, pull request metrics, and AI impact.

GitKraken CTO Eric Amodio breaks down the Model Context Protocol (MCP) and explains why Git intelligence is critical for AI agents at GitKon 2025. In this session, Eric covers: What MCP is and why every major AI company adopted it Why AI needs Git history, not just file system access How GitKraken MCP removes Git pain safely The future of agentic developer workflows How Commit Composer uses AI to organize commits without losing data.

A BGP session goes down because a transit provider exceeded the maximum prefix limit. How do you find the root cause — fast? In this 10-minute demo, we walk through two approaches using Kentik AI Advisor. First, we troubleshoot step by step using natural language: asking AI Advisor to identify the affected interface, check for interface flapping, and review syslog messages until we find the maximum-prefix violation. Then we show how custom network context and natural language runbooks let AI Advisor do the entire investigation autonomously — following the same four steps a senior engineer would.

AI operates on the same systems and infrastructure as every application, which means if you want to keep it reliable, you have to keep the systems underneath it reliable. Gremlin CEO Kolton Andrus explains more in this clip from an AI reliability roundtable with @nobl9inc and @Pagerduty.