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AI can write your Terraform in seconds. Learn what it takes to govern configuration drift and heal AI-generated infrastructure builds.

"We'll just build it ourselves" is usually said with confidence and a rough headcount estimate. Six months later, the team that was supposed to spend two sprints on it is still maintaining it a year in, and the platform that was meant to save engineering time has become one of the things engineering time goes to. Build isn't automatically the wrong call. Sometimes it's the right one.

How to delete a SharePoint or Teams-connected site: admin center and PowerShell steps, the 93-day window, permanent deletion and the storage effect.

Git worktrees let you check out multiple branches of the same repository in separate directories. Each worktree has its own files and staging area, but they all share the same Git history. You can work on several branches without repeatedly switching between them, stashing changes, or cloning the repository again. Worktrees are especially useful when working with coding agents. Tools like Claude Code, Codex, and Cursor can use them to handle multiple tasks in parallel without editing the same files.

Agentic Pipelines now integrates with the redesigned Atlassian MCP server for Claude and Codex agents. Rovo Dev CLI agents in Agentic Pipelines currently use the original version of Atlassian MCP server and are unaffected by this update.

You can already break down cloud spend by team, product, or customer in Explorer. But AI spend has been harder to decompose. Which models is each team running? What activity is driving the cost growth you saw this month? Before AI Explorer, answering those questions meant exporting raw event data and building your own breakdowns, or waiting days for reconciled billing to land in Explorer.

Enterprises are putting hard ceilings on AI spend. Accenture now centrally funds $29 a month per person for Claude and charges anything above that to a project budget code. Uber used up its 2026 AI budget in four months and restricted access. A cap stops waste and useful work at the same rate. The alternative is seeing AI cost by team, by project and by outcome, then funding what returns. Companies spent the past year telling employees to use more AI. Now a lot of them are putting a lid on it.