Skip to main content

GitLab Previews Revamped DevOps Platfom for the Agentic AI Era

GitLab today, at its Transcend 2026 conference, launched a bevy of updates that collectively optimize its integrated DevOps platform for the volume of code being generated by artificial intelligence (AI) coding tools.

At the core of that effort is a Next Generation Source Code Management, a re-vamped implementation of the Git protocol that is based on a distributed architecture that makes it possible to scale DevOps workflows in a way that enables AI agents to complete tasks 50 times faster

Manav Khurana, chief product and marketing officer for GitLab, said this implementation of the Git protocol takes advantage of distributed processing and storage engines to meet the requirements of DevOps workflows that are increasingly being driven by AI coding tools and associated agents. Additionally, GitLab is also now able to limit server-side queries to only what the actual task requires.

At the same time, GitLab is adding a context graph, dubbed GitLab Orbit, that maps code, work items, pipelines, deployments, and production signals in a way that AI coding tools and agents can query. Available in a public beta, that capability is crucial in the age of AI because it reduces the number of tokens that would otherwise need to be consumed to complete each task, said Khurana. Overall, GitLab Orbit can deliver 11 times faster responses while requiring up to 4.5 times fewer tokens, the company claims.

GitLab is also making available in private beta an AI Governance framework that assigns an identity, a policy path, and an audit record to each and every agent action. DevSecOps teams are now provided with real-time visibility into agent inputs, reasoning, tool calls, and anomalous patterns across the organization. They can also apply policies, such as requiring code scans, across the entire workflow, said Khurana.

Finally, GitLab is rolling out GitLab Flex, a licensing option that combines seats and AI credits in a single annual commitment. DevOps teams can shift spend as needed without having to negotiate contracts between these two as needs change, without the need for contract renegotiation, while finance teams are provided with tools that track consumption against the plan.

These capabilities collectively will enable software engineering teams to benefit from AI coding without the current level of chaos, including cost overruns, security and reliability issues, that many organizations are now experiencing, said Khurana. By providing the tools they need to manage agentic workflows, software engineering teams via a highly integrated GitLab platform remain in control of the DevOps loop, he added.

Mitch Ashley, vice president and practice lead for software lifecycle engineering at The Futurum Group, said the DevOps platform is becoming the control plane for agentic software engineering. GitLab’s Transcend updates attach identity, policy, and an audit record to every agent action, shifting the platform from automating pipelines to governing the agents inside them, he added.

Engineering and DevSecOps teams now evaluate platforms on whether agent actions can be attributed, observed, and governed at the action level, with the autonomy granted being bounded by the visibility their platform provides into agent reasoning and tool calls, said Ashley.

It’s not clear to what degree DevOps teams might be considering new tools and platforms for the age of agentic, but many of the workflows that are relied on today were clearly designed for a previous era that did not see nearly as much code moving through the pipeline. The challenge and the opportunity now is not just simply to generate code faster but, more importantly, to dramatically increase the percentage of that code that ultimately winds up safely running in a production environment.



from DevOps.com https://ift.tt/jvDmLQx
Labels:

Comments

Post a Comment

Popular posts from this blog

Mistral Moves Coding Agents to the Cloud — and Gets Out of Your Way

For the past year or so, AI coding agents have been tethered to your local machine. You kick off a task, watch the terminal, and babysit every step. It works — but it’s not exactly hands-free. Mistral just changed that. On April 29, the Paris-based AI company announced remote coding agents for its Vibe platform, powered by a new model called Mistral Medium 3.5. The idea is simple: Instead of running coding sessions on your laptop, they now run in the cloud — asynchronously, in parallel, and without you watching over them. What’s Actually New Coding sessions can now work through long tasks while you’re away. Many can run in parallel, and you no longer become the bottleneck at every step the agent takes. That’s the core pitch. You start a task from the Mistral Vibe CLI or directly from Le Chat — Mistral’s AI assistant — and the agent handles the rest. When it’s done, it opens a pull request on GitHub and notifies you, so you review the result inste...
Post a Comment
Read more

Why the Software Development Tools you Choose Directly Affect Your CI/CD Reliability 

Most conversations about CI/CD reliability start in the wrong place. Teams debug flaky pipelines, investigate intermittent failures, tune alerting thresholds and optimize build times. All of that work is legitimate. However, the decisions that most directly determine whether a CI/CD pipeline is reliable or not were made months or years earlier, during tool selection. By the time teams are debugging pipeline reliability, they are usually dealing with the downstream consequences of upstream decisions that seemed reasonable at the time.   The software development tools a team chooses shape their CI/CD pipeline in ways that are not always visible during evaluation. Understanding those connections is the most practical starting point for teams that want reliable pipelines rather than better pipeline firefighting.   The Integration Surface Problem   Every tool in a software development stack creates an integration surface. Integration surface is the set of connections a tool has with oth...
Post a Comment
Read more

Co-Developing an AI Native Observability Platform  

As AI capabilities continue to evolve, AI is becoming central to managing the growing complexity of distributed, hybrid enterprise environments, enabling more effective analysis, correlation, and automation across interconnected systems.   Traditional infrastructure and specifically network monitoring approaches, often built around siloed tools and static thresholds, struggle to keep pace with the scale, velocity, and interdependencies of modern systems. Further blurring the boundaries between network, application, and infrastructure domains makes it harder to isolate root causes and maintain operational resilience. In this context, AIOps platforms have emerged as one response to the growing need for integrated observability, automation, and data-driven decision-making.   At AI Field Day, Selector AI presented an AIOps platform, which can be considered a foundation for co-creating more adaptive and data-driven network operations. Rather than positioning it purely as a product choice,...
Post a Comment
Read more