Operations | Monitoring | ITSM | DevOps | Cloud

Tamar Bercovici has been at Box for 15 years. She leads the core platform, the backend layer that storage, search, metadata, and AI capabilities all run on. When her systems go down, Box goes down. On a recent episode of the Braintrust podcast, she said the debate around AI-generated code tends to focus on whether the models will write clean code and/or introduce bugs. Tamar's focus is somewhere else entirely.

A little while ago, when agents were telling me “You’re absolutely right!”, I was building webvitals.com. You put in a URL, it kicks off an API request to a Next.js API route that invokes an agent with a few tools to scan it and provide AI generated suggestions to improve your… you guessed it… Web Vitals. Do we even care about these anymore?

Enterprises expect AI to improve how they operate, yet many underestimate the level of clarity required for intelligent systems to perform reliably. AI-assisted operations demand input signals that are accurate, consistent, and interpretable. They require a unified understanding of how services behave, how disruptions originate, and how decisions influence downstream outcomes. This level of coherence is impossible without operational truth.

Chatbots are becoming ubiquitous. Customer support, internal knowledge bases, developer tools, healthcare portals - if it has a user interface, someone is shipping a conversational AI layer on top of it. And the pace is only accelerating. But here's the problem nobody wants to talk about: we still don’t have a reliable way to test these chatbots at scale. Not because testing is new to us. We've been testing software for decades.

AI is quickly becoming part of the engineering workflow. Teams are experimenting with assistants and agents that can answer questions, investigate incidents, suggest changes, and automate parts of software delivery. But there is a problem hiding underneath all of that momentum. Most engineering environments were not built to give AI the context it needs. In many organizations, the service catalog lives in one place. Deployment data lives in another. Incident history sits in a separate system.

Cluster API (CAPI) is transforming how organizations deploy and manage fleets of Kubernetes clusters by introducing declarative, Kubernetes-style APIs to automate cluster provisioning and lifecycle management. While CAPI excels at creating consistent and repeatable cluster deployments across different infrastructure providers, operating it at a massive scale introduces unique day-to-day challenges.

OrionIQ is Logz.io’s new agentic observability platform designed to move teams from detecting issues to resolving them automatically. As AI accelerates software development, operations remain manual: engineers still wake up at 2 a.m. to investigate alerts and rebuild context. OrionIQ uses AI agents to analyze real-time telemetry, investigate incidents, identify root causes, and take action across systems.

Most companies have built AI into their product by now, and many consider it the central feature of what they’re building. But plenty of those same companies are still figuring out how to get their own engineering teams to actually use AI tools day to day. When Loïc Houssier joined Superhuman as CTO in early 2025, his team was in that exact spot. The company had been shipping AI email features for years, but internal adoption of AI dev tools was still early.

Why did your build fail? Ask Rovo, get a clear answer, and even a way to fix it, from anywhere in Bitbucket Pipeline debugging is one of the most common and most painful parts of the development workflow. In our Atlassian research: AI adoption is rising, but friction persists, over 50% of developers reported losing more than 10 hours each week searching for information, onboarding to new code, or toggling between apps.