Operations | Monitoring | ITSM | DevOps | Cloud

Remote work has changed more than where we work. It has reshaped how teams collaborate, how organizations manage performance, and how employees balance professional and personal responsibilities. Now, AI is adding another dimension to this transformation. From AI assistants that summarize meetings to automation tools that handle repetitive tasks, technology is making remote work more efficient in several ways.

For enterprise IT leaders, the question is no longer whether AI agents can help run IT operations, but how to scale them beyond pilots and into the everyday work of keeping the business online. It’s no coincidence that this year’s Gartner IT Infrastructure, Operations & Cloud Strategies Conference (IOCS) opens with a keynote dedicated to scaling agentic AI to enable autonomous IT operations. At BigPanda, we’ve seen a shift from the ITOps leaders we work with every day.

Honeycomb recently launched Canvas connectors, with Checkly serving among its first group of integration partners. With the Honeycomb Canvas connector for Checkly, the Canvas Agent can bring your synthetic monitoring into an investigation to help determine which checks are failing, from which locations, since when, and what Rocky AI identified as the likely root cause.

That was the question posed at the Women in Open Source+ Panel I had been asked to moderate. The panelists, from Snowflake, AWS, and NVIDIA, gave similar answers: We have our jobs today because of the community, and I don’t see that changing any time soon. I felt the same. When I joined Aiven, its direct callout to open source was a deciding factor.

Your employees have access to AI. They've completed the training. Their usage is climbing, but has the way they work actually changed?

AI coding tools have changed how fast software ships. They haven’t changed who runs it. Every extra release, alert and incident still lands on the same SRE, Platform and DevOps teams, and 78% of technology leaders report more incidents since adopting AI-generated code. So operations teams are turning to agents too. Six in 10 enterprise leaders are piloting, deploying or evaluating automated incident response, according to Caylent.

Agents have quickly become part of the tech landscape as organizations embrace AI across their businesses. But embracing agents also means embracing the monitoring best practices that have long been part of engineers' workflows: tracking performance, catching errors, and responding to incidents But it doesn't stop there. Agents also introduce new challenges.