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Connect Bitbucket Cloud to your AI tools with Atlassian MCP

Developers use Bitbucket Cloud every day to turn plans into working software. Code lives in repositories, feedback happens in pull requests, and Bitbucket Pipelines helps teams validate changes before they move forward. For an AI assistant to help with this work, it needs access to the same information. Without a direct connection, developers have to gather pull request details, pipeline results, logs, and links before the assistant can help.

Stop Adding HR Tools. Start Using the Platform You Already Have | Atlassian Webinar

Tired of spreadsheet trackers, lost Slack requests, and SaaS sprawl? Learn how to turn your existing Atlassian stack—Jira, Confluence, and Jira Service Management—into a unified HR hub for recruiting, zero-touch onboarding, and compliance. Most companies run on Atlassian, but HR workflows often remain scattered across spreadsheets, endless email threads, and disconnected SaaS tools. The answer isn't another point solution or a costly rip-and-replace—it's connecting the systems you already have.

How we accelerated code review in a large monorepo using Agentic Pipelines

Last quarter, our AI reviewer told a developer who was fixing a CSS bug to rethink how their component fetched data. The comment was well written and confidently worded. It was also about code they hadn’t touched. That’s what tends to happen when you hand an agent every team’s review guidelines at once.

Dave Killeen: What to Do When Execs Demand AI

Dave Killeen will tell you, on record, that his relationship with AI has crossed into unhealthy territory. He also says he has never felt more alive, that he is 50 and feels 30, and that he sometimes waits for his two small children to go to bed so he can start a coding session. In between, he gives Axel Sooriah the most practical answer on this show yet to the question every product team is facing: your exec says "we need AI," and you have no idea what they actually want.

How to Use Jira Planner to Plan Software Projects

Jira Planner is an AI planning agent in Jira that turns high-level product requirements into ready-to-build plans (requirements, technical specs, and Jira work items) grounded in your actual codebase, architecture, and Confluence context. In this video, you'll see how Jira Planner brings due diligence to the pre-build phase, so engineers and AI coding agents start from a plan that reflects the real code instead of a vague prompt. It's useful for product managers, engineering leads, and developers who want to cut the rework that comes from AI guessing at requirements.

How we automated feature-flag cleanup with Agentic Pipelines

The hard part of a feature flag is rarely adding it. It is remembering to remove it months later, when the rollout is over, the original context has faded, and there is always a more urgent piece of work waiting. Since April 2026, one Atlassian team has used Agentic Pipelines to clean up their monthly backlog of stale feature flags. The workflow prepares the change and opens a pull request, while engineers still review and merge the pull request.

Find code faster: Introducing our new & improved search experience

Finding code across your repositories in Bitbucket just got a major upgrade. We’ve rolled out code search in open beta for Bitbucket Cloud: a faster, more integrated search experience built to help you find code across your workspace without interrupting your workflow. For many developers, search is one of the fastest ways to explore an unfamiliar codebase, investigate an incident, audit API usage, or scope a refactor.

A simpler way to run AI agents in Bitbucket Pipelines

AI agents can help investigate failed builds, fix flaky tests and automate other development tasks. But setting up those agents has required more Pipelines configuration than it should. Agent-powered steps often need different compute, permissions and runtime settings from ordinary build and test steps. Until now, teams have either repeated those settings across every agent-powered step or tried to make one set of global defaults work for everything.

Monitor test health at a glance in Bitbucket Tests

When a team relies on automated tests in CI/CD, knowing that tests ran is only the beginning. Understanding whether the suite is healthy, which tests need attention, and how a specific test has behaved over time — that’s what drives action. Bitbucket Tests is evolving to make those answers easier to find and give you tools to improve your test health.

Driving Impact and AI Adoption as an FDE

Enterprise AI is only useful when it actually runs in production, inside the tools teams already depend on. Getting there is harder than it sounds. Forward Deployed Engineers at Atlassian work directly inside some of the world's largest organizations, building AI-powered agents, connectors, and workflows on Atlassian's platform. They work alongside customers to understand the real constraints, design solutions that hold up at scale, and see them through to deployment.