Operations | Monitoring | ITSM | DevOps | Cloud

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.

In 2025, attackers reached the Salesforce data of hundreds of companies without breaking into Salesforce. They used access tokens belonging to an AI chat agent. Google's threat intelligence team counted more than 700 potentially affected organizations, and because the tokens belonged to a trusted app, they carried the attackers past safeguards such as MFA. Enterprises already know how to manage this kind of risk for people.

AI Signals adds a standard set of AI dimensions to your dimension picker, plus custom dimensions built from your own data. Not every team needs all of them. The ones you don’t use make the ones you want harder to find. Cleaning that up used to require an engineer to run a script every time an account team needed a change. We identified the bottleneck and automated it, so account teams can manage dimensions themselves and engineering stays focused on product work.

AI cost per query is your total AI serving spend divided by the number of queries served over the same period. It combines model API charges (input and output tokens), retrieval and orchestration overhead, and shared infrastructure. Most production text queries cost between $0.0005 and $0.02. It's the base unit of AI unit economics, and the number finance needs before any ROI claim holds up.