Operations | Monitoring | ITSM | DevOps | Cloud

Spec-driven development with AI coding agents could change how you build software. In this GitKon 2025 talk, Erik Hanchett, Senior Developer Advocate at AWS, breaks down why AI coding assistants perform dramatically better when they start with structured specifications instead of raw prompts. If you've been vibe coding your way through complex features and wondering why your AI keeps going off the rails, this is the video for you.

Most enterprise SaaS products, like Komodor’s Autonomous AI SRE Platform, require installing a remote agent on the customer’s infrastructure, which varies significantly from one organization to another, in terms of architecture, configurations, permissions, processes, and more. This “unmanaged” model creates major blind spots, making daily operations, observability, debugging, and incident response challenging. When failures occur, limited visibility and bespoke systems make root-cause analysis slow, incomplete, or impossible.

When your distributed system goes down, your team spends days sorting through noise. That is revenue walking out the door. In this video, Jasper Paul breaks down the event correlation engine built to eliminate alert fatigue, and accelerate root cause analysis. Most monitoring tools still rely on basic time-window alert grouping — clustering alerts that fire at the same time and calling it correlation. But in a distributed system, outages are never isolated events. And grouping symptoms doesn't find root causes.

AI isn’t the real threat to IT careers. The real risk? Not building the skills that AI can augment. Jon Collins breaks down the “AI augmentation gap.”

The Flare CLI connects to your Flare performance monitoring data and uses AI to turn it into actionable insights, right from your terminal. In this video, you'll see how a single command pulls your real performance data from Flare, then generates a full review: identifying slow endpoints, spotting error trends, and suggesting concrete fixes. Links.