Operations | Monitoring | ITSM | DevOps | Cloud

For today's modern businesses, the data landscape demands security and flexibility. You need to connect your observability platform to rich, proprietary datasets that often reside in private networks without compromising security or managing complex network infrastructure. You may also face an extra layer of complexity in order to effectively query and visualize that data. Luckily, modern artificial intelligence tools have made these previously complicated processes much simpler.

The Atlassian Rovo Model Context Protocol MCP Server now supports Bitbucket Cloud. AI clients like Claude, ChatGPT, Cursor, and VS Code can now browse repositories, create commits, open pull requests, and check pipeline results, all through the same secure MCP connection that already works with Jira and Confluence.

In 2026, I think it’s safe to say that most mobile devices in enterprise organizations aren’t purchased just for their ability to make calls. And for millions of employees, especially frontline workers, their primary device isn’t even a laptop anymore - it’s a smartphone or tablet. Yet, mobile device insights have largely remained a blind spot for IT.

Three months ago, a CIO told me her organization had “already deployed agents.” Her endpoint team assumed she meant the telemetry clients on every managed laptop. Her service desk thought she meant AI chatbots. Meanwhile, her security architect heard “autonomous decision-making.” They were all right and all talking past each other. This is the agent confusion problem.

Most operational waste comes from fragmented workflows rather than individual performance constraints. An incident begins long before any fix is applied. Alerts trigger, tickets open, and engineers start reconstructing context across systems that were never designed to operate as one. Logs, metrics, past incidents, and runbooks sit in separate tools, each requiring manual lookup, interpretation, and validation before any decision can be made.