Operations | Monitoring | ITSM | DevOps | Cloud

Rovo makes AI-native teamwork real for the enterprise

AI-native teamwork is here. With your team's context connected via the Teamwork Graph, Rovo moves beyond “answer this” to “take this on” with: Max mode in Rovo Chat that completes complex tasks autonomously (coming soon!) The new, unified builder experience in Rovo Studio is now generally available to put your AI to work. Teamwork Graph-powered agents are now available across your entire stack. New enterprise-grade controls to manage and secure agents at scale.

The Role of AI Chatbots in Modern DevOps Incident Response

Modern DevOps environments demand speed, accuracy, and continuous availability, especially when incidents disrupt critical systems. As organizations scale their infrastructure, traditional response methods often struggle to keep pace with the volume and complexity of alerts. This is where intelligent AI chatbots for customer support are becoming essential, as they provide real-time conversational interfaces that connect teams to automated workflows, incident data, and resolution tools, much like the capabilities showcased in advanced enterprise conversational AI platforms.

AI Enablement for Dev Teams: The 6-Pillar Flywheel

AI adoption is already happening on your team, whether you have a strategy or not. Tracy Lee (CEO of This Dot Labs, Microsoft MVP, Google Developer Expert) breaks down the AI Enablement Flywheel — a 6-pillar framework used by successful engineering organizations to move from scattered experimentation to scalable, ROI-positive AI workflows.

DORA Metrics in the AI Era: Why Deployment Isn't Faster

DORA metrics in the AI era reveal a paradox: PR volume is climbing, but deployment frequency is staying flat. In this talk, GitKraken's Director of Product Jeff Schinella breaks down why AI-accelerated code generation is creating a review bottleneck that your DORA metrics can't fully explain on their own. Jeff walks through how PR metrics (cycle time, first response time, code churn, and PR size) serve as the leading indicators behind your DORA data. If your deployment frequency is flat while PR counts go up, the bottleneck isn't your devs. It's your review capacity.

GitKraken Desktop in 6 Minutes: Open a Repo, Run an Agent, Ship the Change

The fastest way to get up and running in GitKraken Desktop. In this tutorial, you'll open a repo, start an AI coding agent in its own worktree, review the agent's changes against your own work, and ship a pull request without leaving the app. What you'll learn: Chapters Help Center: help.gitkraken.com.

Share artifacts between parent and child pipelines | Bitbucket Blitz | Atlassian

Bitbucket Pipelines lets you build reusable pipelines and share them across repositories. These reusable, shared pipelines need a way to share artifacts. Otherwise, we’ll have to repeat expensive steps such as downloading and installing dependencies and building the application code. You can now specify an artifacts section for child pipelines, with upload and download keywords. Artifacts listed under upload will be moved from the parent pipeline into the child pipeline, where they can be used and potentially modified.

Split your Bitbucket Pipelines workflows across multiple files | Bitbucket Blitz | Atlassian

Building and maintaining a 2000+ line bitbucket-pipelines.yml can be a lot of work. Now you can split large bitbucket-pipelines.yml files into multiple, smaller pipelines.yml files. These smaller files can be composed via shared pipeline syntax to replicate the functionality of the original bitbucket-pipelines.yml file. They can also be shared with and reused in other repositories.

90% AI Adoption. Still Failing. DORA Explains Why.

AI adoption is nearly universal. So why are most teams still struggling? In this session from GitKon, Nathen Harvey, head of DORA at Google Cloud, shares findings from the 2025 DORA State of AI-Assisted Software Development report, drawing on data from nearly 5,000 developers worldwide. The answer isn't more AI. It's what surrounds it.

Why Mandating AI Tools Backfires on Engineering Teams

Responsible AI adoption for engineering teams starts with culture, not compliance. In this GitKon talk, Rizel Scarlett (Tech Lead of Open Source DevRel at Block) shares how Block helped thousands of engineers actually want to use AI tools, including Goose, Cursor, Claude Code, and more, without mandates, vibe coding disasters, or security gaps.

Your Developers Feel More Productive. Your Codebase Disagrees.

AI adoption is up. Developer confidence is up. So why is code duplication up 10x since 2022? GitKraken VP of Developer Research Jeremy Castile shares the frameworks we built after analyzing 211 million lines of code and talking to hundreds of engineering teams. This is the playbook version of the research — practical, not theoretical. In this session, you'll learn: The gap between how productive developers feel and what's actually happening in the codebase is real. If you can't measure it, you're just guessing. Nobody wants to be guessing with this stuff.