Operations | Monitoring | ITSM | DevOps | Cloud

When incidents pile up fast enough, every part of the company bleeds: support is fielding angry customers, AEs are on apology calls, and engineering is burning cycles on retrospectives instead of shipping. For Eran Kampf (VP of Engineering at Twingate) where the product is the network, that was the moment he made a call most engineering leaders won't: stop all feature work for a quarter and fix reliability.

QA Lead at Rootly explains how she's embraced AI to keep up with the pace of AI-driven feature development.

SRE Lead Ricard Bejarano (Cisco) and Jorge Lainfiesta (Rootly) sit down to talk about a recent intermittent incident that had the team scratching their heads.

During an incident, ask the Rootly Agent anything and it'll respond (and act) based on context and your data. Use the Rootly Agent to: The Rootly Agent performs actions on your behalf, so it is bound by the permissions assigned to your user. It will also ask for confirmation before taking significant actions. Rootly admins can turn it on for their workplaces and start running incidents even more efficiently.

The debate about whether AI can write production code is over. Companies are handing work to fleets of agents, and for many, they write most of the code that ships to production. The next challenge is everything that happens once an entire engineering organization runs this way, at full speed. Teams that generate code 10x faster still review it at human speed, and that mismatch is now the constraint. Code ownership is also becoming an issue, as developers learn to trust agentic processes a little too much. When an agent breaks production, who is responsible?

Every pilot who's never had an engine failure is still ready for one. The same can't be said for most software engineers facing their first major incident. Hamed Silatani, co-founder and CEO of Uptime Labs, and former Head of Reliability Engineering at IG Group, has spent two decades watching engineers learn incident response the hard way: alone, under pressure, with no training.

Last year, we went fully agent-based at Rootly. Cursor, Claude Code, Codex, all of it. The productivity gains were real. However, Rigel, senior engineering manager at Rootly, started noticing a pattern emerging in his team.

For nine years, Maria Vechtomova was shouting about monitoring. Nobody cared, until LLMs arrived. As co-founder of Cauchy, Databricks MVP, and one of the most followed voices in MLOps, Maria has watched the field evolve from hand-built experiment trackers to today's flood of observability tools, and her central claim might surprise you: globally, nothing has changed. The fundamentals are the same: track your code, data, and models so you can roll back when something breaks.

AI can generate a UI in seconds. So what do designers actually bring to the table? Marcela, Principal Product Designer at Rootly and former Founding Designer at Ramp, has spent 20 years in design. Her answer: taste isn't about aesthetics or crafting pleasant interactions. It's about asking the uncomfortable questions, and choosing the right problem, not the easiest one.

Every engineering org says they want to improve reliability — but most can't even agree on what "good" looks like. Ganesh Datta, Co-Founder and CTO of Cortex, has spent the better part of a decade helping companies confront that gap.