GitLab Inc. (GTLB) Earnings Call Transcript & Summary

Hey, everyone. It's Bill. Welcome to GitLab Transcend. We're hosting our community in 12 cities around the world with thousands more tuning in online. Thanks so much for joining us today. Growing up on a small farm in rural Utah, I loved science fiction. And I remember dreaming of the day when machines could interact with us like our friends and family and colleagues at work and elevate our lives in really profound ways. GitLab started its journey over 14 years ago, focused on bringing the software development life cycle on a unified platform with modern agile principles. We serve more than 50 million users and over 100,000 organizations worldwide today. But in many ways, it feels like we're just getting started. As you can tell from our event title, we are seeing software engineering rapidly transcend the evolutionary improvements of the past decades. This is the most exciting time ever to build software and to power software-driven businesses. For us, it's about bringing everything our customers love about GitLab into the AI era. If you're a technology leader tuning in, I'm sure one of your primary questions is how you can increase the innovation velocity of your teams and get real ROI with your AI investments. I've been in your shoes building software for decades, and I truly believe AI holds the potential for step function acceleration and innovation. And even though I'm a CEO of a company that builds AI technology for engineers, I can tell you this innovation velocity won't come from just adding AI tools or issuing top-down mandates on AI. That's because our main challenge in building software is not just a tooling problem. It's a really complex people, process and technology problem. Software teams need to get comfortable with using AI. Their workflows need to be redesigned for agentic collaboration, and they need AI across the entire software life cycle. This transformation needs to happen while maintaining the quality, the security and the compliance of software we already have in place today. Let me show you why. Let's start by talking openly about some ugly truths in software engineering. Let's put ourselves in the developer's shoes for just a few minutes. It's Monday morning. You open your laptop, coffee in hand, ready to go. You're a software engineer. You write software, right? Well, that's theoretically the job. But first, look at your calendar, sprint planning, backlog grooming, design reviews, architecture reviews and that daily standup that somehow takes 45 minutes. The average software engineer spends up to 11 hours per week in meetings. That's nearly 1/3 of your work week gone before you write a single line of code, and there's more. One study of 250,000 developers found they spend just 52 minutes per day actually coding, 52 minutes. That's less time than it takes to watch a Netflix episode. So you finally escape your meeting and it's now 2 p.m., which statistically is when 45% well code actually happens and you start coding, you're in the zone, you're flowing and then Slack pings, a quick question. There's no such thing as a quick question. Research shows it takes 23 minutes to refocus after an interruption. That question just cost you half an hour of work. So you finally finish the code. You create a merge request. It sits there on average for 5 days waiting for all the approvals. Did it merge? No, like a flip of the coin, 50% of the time, the pipeline fails, flaky tests, environmental drift, the build that worked yesterday, but for some unknown reasons, it doesn't work today. Okay. So you moved past that issue. But now the SaaS scanner flags 500 vulnerabilities. You're losing context. A survey found 62% of developers would rather reduce false positives than catch more real issues. That's how bad the noise is. Eventually, you just stop listening. And beneath all of that, works technical debt. Engineers spend 84% of their time on maintenance, on tech debt and only 16% building new features despite 93% saying building new things is the most rewarding part of the job. So let's recap Monday. Meetings, waiting for events, pipeline failures, security false positives, bug triage, technical debt and somewhere, if you're lucky, 52 minutes of actual coding. And tomorrow, we'll do it all again. But here's the thing. It doesn't have to be that way. AI coding tools have reenergized software engineering because they promised 10x productivity gains. And that may be true for some people, but only for the 52 minutes a day that they actually spend coding. Let's unravel our software life cycle and see how that plays out in a real-world project. I'll play a project manager for just a minute and pull up everyone's favorite Gantt chart. Here is a representative project with approximate time spent by developers within each stage. And here's what it looks like with AI coding tools. This is what we call the AI paradox. You see for a given developer's project, where they only spend 10% to 20% of their time coding, you see that even if you're 10x that slice, you've only improved total delivery by a little bit. Meanwhile, the real bottlenecks haven't moved. Your developers are now just faster at getting stuck in the same queues. That's because while the technology for one stage of the software life cycle has advanced, we need to advance all stages, and streamline the people and process handoffs across every stage. With the help of AI, it's time to move from stage-based software life cycle, full of people and process handoffs to continuous software development, where agents can run continually, managing the handoffs and iterations, while humans remain above the loop, intelligently orchestrating their agents across the software development process. That's what we're building towards. Let's envision what that might look like. Today, a GitLab Epic is a way to define a large project for humans to manually execute. Tomorrow, it's a spec for your agents. You create the Epic with the help of agents. GitLab Duo Agent Platform reads it, ask clarifying questions, breaks it into subtasks, respond to the right agents and links commits and merge requests automatically. You orchestrate the work, you define the intent and steer it. Here's a critical principle. Agents don't decide if code is good, pipelines do. Just like today, when an agent pushes code, GitLab CI becomes the objective definitive arbiter, tests run, scans execute governance checks, verify compliance. The pipeline doesn't care that an AI agent wrote the code, it applies the same rigorous validation applied to any commit. And here's where GitLab's unified platform differs. System events are raised Azure work flows through GitLab triggering more agents and flows to automatically pick up and handoff work as needed, 24/7, 365 days a year. This enables software engineers to be in a continuous flow, and it enables their agents to continuously iterate and improve until they meet business requirements. Every change has artifacts. Images, packages, software, bill of materials, providence, signatures because GitLab manages the entire life cycle, every artifact is automatically traceable to its source and validated by policy. Security and compliance aren't separate workflows. They happen continuously as software is built. Audits become queries. Risks become manageable and visible. Supply chain risk becomes manageable at scale. A deployment using GitLab is not the end of delivery. That's where the learning begins. Every release is progressive by default, aware of the environment, policy and impact signals from production flow directly back into the platform. Performance, errors and user impact becomes inputs. Not afterthoughts. Agents observe and recommend or even decide based on your policy, whether to proceed, pause, roll back or roll forward. You move to consolidate the stage-based life cycle filled with people and process handoffs at every step towards a continuous innovation loop where agents help your team go faster. Not just faster coding, but higher leverage, more effective software innovation. This changes more than developer productivity. It changes the economics of teams. We would love to show you this pace of innovation in action. Let me hand it over to John, Fatima and Cesar for a demo of intelligent orchestration.

Thanks, Bill. There's so much work that goes into building software across teams beyond coding. And when you're doing that work manually, it holds you back from shipping faster. Let me show you how intelligent orchestration can ease these bottlenecks in real life. I'm John, a project lead and this is my dev team.

Hi, I'm Fatima.

Hello, there. I am Cesar.

We're going to tackle some challenges in one of our projects. Doing all this manually would take hours. We want to show you what we can do in a few minutes. So we're working on an application that generates Aura cards. Currently, the Aura card is based on the name and role that you enter into this form, a card with a random Aura is generated for you to download the share. But this is 2026. So we want to add cloud integration. With these integrations, we can generate custom Auras based on data from your GitLab profile and add an enhanced version of your profile picture to really personalize the card for you. Of course, we also need to make sure it's secure before we can ship it. We've got a lot to do, but I think we can pull this off. So here's the Aura agent project. This contains not just our code, but everything that goes into building a production-ready application, security scan results, epics, issues, pipelines, analytics, dashboards, all the good stuff. Look at all these issues. Thankfully, I have a foundational agent built by GitLab to help me out. I'll enter my prompt. What should we work on to get this ready to ship? Duo Planner Agent has access to tools and context from across the project. This agent is purpose-built to help with planning and product management. It makes my life so much easier. And here comes the plan. It normally would have taken me at least 30 minutes of scrolling issues and comments just to understand what's going on. Now I don't even need to look at my issue list. I can access this agent from anywhere in GitLab. Planner identifies the issue for making our app agentic as top priority, agreed. Let me assign this issue to Fatima, who is the developer for this app.

I am on it, boss.

Thank you. Appreciate it. All right. Now I'm going to check out the open MRs to see if we have any work in progress that needs attention. I see there's a failed pipeline in merge request 32. Our data shows that pipeline failure rates can be as high as 50% for some teams. You can imagine how much time and money is spent on these failing pipelines. Cesar owns testing and deployments on this project. So I'm going to assign this MR to him to review.

I'll start working on it right away.

Thanks. Fatima, how are things going with the issue?

Hey, things are moving along. I decided to use Duo CLI for this because I live in my terminal. Duo CLI Is our AI native assistant for the command line, which we are currently dogfooding and will be available to the public as an experiment in the next few weeks. With Duo CLI, I can ask questions about the code or use agentic workflows. And the conversations are synced from the CLI to the UI, so I can talk to the CLI and then pick it up in the UI later.

It's so awesome. I love providing these developer experiences and meeting them where they're at.

Yes, exactly. Now while you were planning, I asked Duo CLI to review the issue and come up with a phased implementation plan and then post it on the issue. So let me show you the plan that it came up with. It has 5 phases from creating the back-end service to the back-end logic, including the fallback strategy and updating the front-end UI and stylization to make this app look AI generated.

All right. That sounds again. Why are we taking a phased approach?

Just like you like to use the Planner Agent to prioritize and aggregate your issues. I like to use Duo CLI to help me break up my work into parts so that I can tackle them one at a time. Okay. So switching back into my terminal. I'm going to ask Duo CLI to start implementing Phase 1 and Phase 2 of the plan. So while this is running, you can see that it's actually doing multiple things. It's reviewing our project structure, reading through the existing code to understand how everything connects and then writing some new code that matches our team's coding patterns.

And it's doing all of that context gathering automatically?

Yes. And that's what I love about Duo CLI because we don't have to manually feed it every file. It will navigate the code base and pull out the information that it needs. And it's like having a really good pair programmer to augment your work. And then when it's done with all the things that you've asked it to do, it will give you a summary of all of the changes across the files. Now whenever I get that summary, I want to look at the code changes before they're committed. So let's take a look at the git diff and walk through that code.

All right. That's great. Tell me more about the changes.

Yes. So we can see that it created a function to pull the data from the GitLab profiles, and then added some validation and implemented the back-end logic for Claude to generate those customized Agent Aura cards. I might need to update some of this code in the generate agent module, but I'll keep working forward with these phases, and I'll let you know when it's ready for our view.

All right. Sounds good. Cesar how it's going with the pipeline?

Yes, John. While Fatima was working. I was able to fix the pipeline failure quickly using one of GitLab's agents. I started by clicking on the Fix pipeline with Duo button. Let's head to the agent session log that ran this flow. Here, we see that the agent took several actions to understand the context and then it generated a pipeline failure context report that includes the test failure summary, merge request changes, root cause analysis and more. The agent uses this report to come up with a pipeline fix plan that provides a root cause, task to fix the pipeline, expected outcome and technical details. The agent then generates a pipeline fix complete section, which includes what was fixed, verification results, and why this works. Finally, the agent creates a brand-new merge request with a corresponding fix.

Nice. Can you walk us through that merge request?

Sure. Let me open it up. So notice that this MR merges into the original request with the broken pipeline. Also under the changes tab, we see the fix that the agent applied to test-api.js file. And under the overview tab, we can see that the pipeline for this MR has run successfully. Notice that the merge has been blocked because this MR is in a draft mode. So let's mark it as ready and merge it. So merging will launch a new pipeline. Let's drill into this running pipeline and the job test API, which was failing before now it should pass.

So a quick question. Why did you use a flow for this?

Well, foundational flows like fixed CI/CD pipeline are designed to solve specific problems and are built and maintained by GitLab. So I know they are tested and production ready.

That's actually my favorite foundational flow because broken pipelines are so difficult to deal with. And GitLab provides a few other flows, such as the Developer Flow, which takes an issue description and writes all of the code for you.

That's right. We also have a Code Review Flow, which I'm going to show you next. Okay. It looks like the pipeline passed. Let me show you the code review now. So Duo has automatically reviewed the updates in this merge request. For code reviews, we are using the custom instructions you see here for this project. This means that the results will be specific to the things our team cares about. I can see comments in this MR that follow our team's coding standards.

So the pipeline is fixed, and we did a code review already. That's awesome. How long would this normally take?

Well, fixing our pipeline could take hours normally, you'd need to determine the root cause of the failure for starters and also read the documentation then apply the fix and then test the pipeline. And with respect to code reviews, if my reviewer is out sick, our code review could be waiting days before they get to it. But with GitLab Duo Agent Platform, both of these can be done in a matter of minutes.

Speaking of a matter of minutes, guess who has everything working for the AI integration.

I'm guessing it's you.

It is me. That's right. Your AI engineer. So I have got the app successfully running on my local host. And as you can see here, I can authenticate via GitLab and the app will generate an Aura card for me. And the Aura that I got is the Merge Request Diplomat, which is on point, given how many MRs I open.

Very nice. So we've got this working. Are we ready to ship to prod?

Not quite, John. Security scans are enabled for this project. And while checking the vulnerability report, I notice that there are three outstanding high severity vulnerabilities, which need to be addressed before shipping the application.

Yes, that's a good idea. So which vulnerability are we going to start with and how are you going to approach it?

So let me work on one of the Server Side Request Forgery vulnerabilities. I'm going to open the Security Analyst Agent and have it work on this. I'm going to ask it, what is the recommended fix for this SSRF vulnerability?

Ooohh, the Security Analyst Agent. I love that agent. As a developer, who understands the value of security, but is not an expert, I really love how that agent breaks down the vulnerability and helps you understand the remediation steps. I always learn a lot when I'm using it.

That's right. The agent responds with a detailed description of the vulnerability, options for how to fix it, implementation priority, and testing checklist. The Security Analyst Agent is giving us options for what to do next. Let's tell it to go ahead and create a merge request to implement this fix.

I love how approvals are required in these agentic ensuring that you have a chance to review the changes before the agent starts making adjustments to your code base or your project or issues.

So from the agent's response, we can click on the link to the newly created MR. We can see that the MR has implemented the fix. And once the pipeline passes, this vulnerability will be resolved and verify that its pipeline has already been kicked off.

Nice job, Cesar.

Thank you, John.

Wow, so we built out the AI integration, fixed a broken pipeline and resolved the security vulnerability in just a few minutes. This shows the power of intelligent orchestration with GitLab. We're going to publish this app for you to go build and share your Aura cards in just a few minutes, and we'll share the link at the end of the event. Stay tuned. Back to you, Bill.

Thanks, team. Isn't that amazing? Hours of work, condensed to minutes, developers delegating work to agents and working in parallel across multiple stages to get things done faster. And these are just a few of the use cases that GitLab can handle today. The real power of intelligent orchestration lies in its extensibility. Teams can create custom agents and flows tailored to their specific engineering standards, compliance requirements and organizational workflows. With hundreds of use cases already documented, GitLab helps transform not just how software teams operate, but drives real business impact. 2025 was all about agentic AI. And I believe 2026 will be all about orchestration. This is the future. Swarms of AI agents working alongside teams across the entire software life cycle. So you may wonder why GitLab? Why not stitch together the latest open source or AI native best-of-breed point solutions that make it to Hacker News every day. We started building our intelligent orchestration vision last summer and Duo Agent Platform represents just the first step in a long-term approach to providing customers the best platform for software engineering. There are 3 key reasons we believe you'll get the best solution from GitLab. First, we're your system of record, your pipelines, your code, your security scans, your issues, merge requests, deployment history, all of that's already here. The problem point solutions have is that AI agents are only as good as the context they can access. A coding agent on your laptop sees local files, but can it see a pipeline that's about to break? The security vulnerability flagged last week? The issue explaining why this code exists in the first place? GitLab connects your entire software life cycle into a single queryable map accessible by both humans and agents. It captures your current source code, pipeline configuration, security and compliance policy rules, issues, Epics, it's all here. And we're also building a historical view that can help agents understand a time line of your projects, giving them critical information about how your software has evolved over time. Why it's changed? And who changed it. This is the level of nuance and quality context that point solutions simply can't match. If you're an existing customer, we're not asking you to adopt something new. We're inviting you to unlock what you already have. Now here's the second reason. In this new AI era, the tools you choose will not only affect your developer experience, but will have an impact on the quality, the speed and the cost of your agents as well. Every time an agent crosses a vendor boundary, authentication handoffs, rate limits, security policy and token management, among other things apply. But even more importantly, you're mixing disconnected data sets with limited context windows, which will directly impact agentic outcomes. What was once just a matter of developer preference can now impact business outcomes. This is the outer loop problem. With GitLab's inner loop architecture, compute and data are co-located in a secure enterprise cloud, one coherent context graph, one permission model. So when an agent fixes a pipeline, it operates within the same system where the pipeline runs and has access to all the same context across the software life cycle that your software engineer has. You simply can't to do enterprise version control on a laptop, running enterprise CI/CD pipelines or compliance auditing locally. Workloads must happen where your software life cycle data lives. And GitLab is where the data lives, which means it's where agents can do real work, not just suggest code, but ship code with full governance, visibility and control. So how do you bring all your existing tools together with GitLab? You use outer loops for exploration and connect to GitLab for inner loop execution. That means your team can keep using their IDE agents and specialist tools and use MCP together with Duo CLI to exchange context and take action. Then GitLab closes the loop where it matters. Pipelines validate, policies are enforced, artifacts are trusted, deployments are controlled and auto trails are complete. You start anywhere and you deliver through GitLab. Our third reason is GitLab provides enterprise-grade protection. Your intellectual property is your most valuable asset. You may not be ready to let agents touch anything or you may want a human in the loop for oversight on specific projects. And for new projects, you may decide to go full agent swarm mode someday and let AI do all the work. No matter where you are in your AI journey, GitLab gives you the level of control that you need, you decide, which users, projects and groups can use AI capabilities. When AI agents operate, you need to know who accessed what? What actions we're taking? And are we still compliant? Every agent action is centrally logged, including reasoning tools called and actions taken, and your proprietary code is never used to train models. One platform, one place to enforce policy, one audit trail, and one security boundary. To share how our customers are modernizing their software life cycle with GitLab, let me pass it over to Sharon.

Hi, everyone. I'm Sherrod Patching, VP of Customer Experience at GitLab. You've seen what the intelligent orchestration can do. Here's what it delivers in the real world. Ericsson achieved 50% faster deployments and saved 130,000 hours over 6 months, translating to faster innovation for telecom operators worldwide. Deutsche Telekom integrated security scanning into their software life cycle. The result release cycles cut from 18 months to 3 months. Barclays grew AI adoption across their software development life cycle with GitLab Duo, resulting in over 80% developer satisfaction, which enabled them to focus on higher value problem solving instead of manual tasks. These modernization journeys aren't theoretical. They're happening right now. To help you accelerate your journey, we're launching a new assessment program next month. It will enable you to measure your organization's speed of software innovation and get customized reports on recommended next steps. This program is built based on real-world lessons learned from more than 300 organizations worldwide with onboarding playbooks, service catalogs, success plan templates and more. We can't wait for you to try them. One of our customers who are partnering with GitLab as part of their modernization journey is Southwest Airlines. Southwest Airlines is one of the largest airlines in the world, operating 4,000 flights per day with 72,000 employees. The technology organization of 3,000 is tasked to ensure the reliability and resilience required for 24/7 operations. Now with the recent investment in GitLab Duo Agent platform, they are taking one more step towards shipping mission-critical software faster. Please join me in welcoming Grant Morris, Managing Director of Technology for Business of IT platforms at Southwest Airlines. Welcome, Grant.

It's a pleasure to be here. And congratulations on your general availability announcement of the Duo Agent Platform.

Thank you. I appreciate it. Your support and partnership along the way has been invaluable. I'd like to share a few notes from our journey together with this audience here. How about a few questions just to guide our discussion.

Absolutely. Sounds good.

I know you've been at Southwest for 26 years. That is an incredible journey. You've seen a lot of change in how software gets built. Can you tell us what you were experiencing when you originally sought out a DevSecOps platform? And also what was important for Southwest and why was GitLab the right strategic partner?

It has been quite a journey over the last 26 years and the way that we build and deliver software has changed immensely. We started off with individually built and deployed applications. And now as we've grown to support over 4,000 flights a day and an engineering community of over 3,000 across the globe, our requirements are much different today than they were in the beginning. AWS and GitLab have become 2 of our strategic partners as we work more aggressively towards a cloud-first ecosystem. And in order to do that, we needed a platform that would scale and also provide the security and the governance that we require being as part of the airline industry. Moving forward, our dedicated deployment option of a single-tenant SaaS offering from GitLab was very important to us. It provided the security and the regulatory compliance that we needed. But beyond the infrastructure and how we utilize the platform, what it really enabled was our teams to leverage the platform capabilities that we provided and instead of having singular siloed teams with each with their own deployment process, really let the application teams focus on the business value that they are trying to deliver while our platform products mature and take that burden off of our application teams. Additionally, we're really challenging ourselves to leverage as much of GitLab's wide array of out-of-the-box capabilities as possible. In addition to the single-tenant SaaS solution, which has taken some of the burden of managing our own self-hosted solution off our plate, we continuously look for our platform engineers to deliver more capabilities to our application teams to further allow them to focus on what matters most, which is delivering business features for their customers.

Awesome. That is an incredible transformation. And now you're adding agentic AI to the mix with your recent investment in the GitLab Duo Agent platform. What are the real problems you're aiming to solve? And how is AI making a difference for software delivery across your organization?

We're looking for use cases such as security scanning and automated CVE remediation, smarter Renovate for our Docker-based images and a better pipeline component upgrade path, leveraging the capabilities of Duo Agent platform. We think if we can have an agentic workflow that is scanning our code bases for security vulnerabilities and then proposing an automated merge request for our developers to review, that will take a lot of toil and one-off time from our developers. We also think that by using something like our Smarter Renovate capabilities, we can reduce the toil associated with the upkeep of our Docker-based images. And then finally, we anticipate that we can automate approximately 90% of our pipeline component upgrades for our customers. Again, all these things using the agentic workflows as part of Duo Agent platform that will reduce the toil and the repetitive task of our engineers and allow them to focus on what's most important, which is delivering business capabilities to our customers.

I love that these are really tangible use cases. I'm looking forward to partnering with you and your team and measuring the impact of these agents to your workflows. So to close our discussion, I'm curious, where do you see this going? What would software engineering look like at Southwest just a few years from now?

I think that's a great question and something that we're really trying to figure out right now. I think from a team perspective, we'll see the advent of more persona-based AI agents. We really want to focus on our engineering productivity and being able to supply our engineering community with specific domain-based personas will allow them to really optimize their workflow. Everything from creating a better story, better user documentation to getting to chat with a security expert or a deployment expert will help them really optimize the software delivery flow around how they want to work, being able to leverage agentic DevOps, not only in a persona-based format, but really to help orchestrate the overall software delivery life cycle, including inputs and agentic signals from our observability solutions because we all know demand comes from everywhere. And we need to be able to have our agentic capabilities receive that demand and have it flow through our normal delivery processes in an accelerated manner. And then finally, imagine being able to have our agentic workflows, look at our repos in the background, propose tech debt upgrades and take care of minor or bigger version upgrades without our developers having to get in and search the code base and really spend time doing that. So we think by leveraging Duo Agent platform and the agentic workflows that it provides, tech debt will eventually become a thing of the past.

That is an incredible vision, especially your comment on making tech debt a thing of the past. I know that is very much desired and sounds like great potential for all of our customers. So thank you again for sharing, and thank you for partnering with us on this journey. It has been an absolute pleasure having you on Transcend. I truly appreciate your time and sharing your insights with everyone tuning in.

Of course. Thank you for having me.

To tell you more about GitLab's intelligent orchestration, let me pass it over to Manav.

Thanks, Sherrod, and thank you, Grant. We're all excited about your vision and appreciate the trust you've placed in GitLab. Hello, everyone. I'm Manav Khurana, GitLab's Chief Product and Marketing Officer. Earlier, Bill talked about the future of software engineering as a continuous innovation loop with AI agents helping your teams go faster, not just faster coding, but higher leverage, more effective software engineering. So let's talk about how you can make that real. Beyond pilots and experimentation across your entire organization. Today, I want to show you how GitLab is making that happen with security built in at scale and inside the guardrails you already operate under. We call it intelligent orchestration. Intelligent orchestration has 3 parts: first, the Agentic Core, the foundation that makes agents actually work inside GitLab; second, unified DevOps and security tooling, where we are deepening the capabilities you already rely on; and third, enterprise guardrails, how you can stay in control while moving faster. Let's start with the Agentic Core. This is the beating heart of GitLab. We began our AI journey a couple of years ago with GitLab Duo, offering code suggestions and chat assistance. It worked. But we learned something really important. You don't have a handful of AI use cases. You have hundreds. And AI added feature by feature does not scale because your work doesn't happen in one tool within GitLab. It happens across entire workflows from idea to production. So in 2025, we made a deliberate shift to a platform approach with Duo Agent platform. So you can orchestrate AI agents across the entire software life cycle using the same context, permissions and security model as your team has in GitLab. To get there, we evolved GitLab in 3 layers: Experience, control and data. At the experience layer, there is agentic chat, which you saw firsthand earlier in the demo. Agentic chat doesn't just answer questions. It reasons across issues, merge requests, pipelines, security findings and a lot more. It's in the GitLab UI, your favorite IDE. And as you saw earlier, soon in the CLI. Instead of having to ask multiple questions to get something done, you get one answer for the task at hand and the action behind it. We've also built foundational agents for the tasks that slow every team down. For example, the planner agent turns a list of issues into structured prioritized work in minutes. The security analyst agent, translates vulnerabilities into plain language that helps your teams fix them faster. And coming soon, the data analyst agent that quickly gives teams insights from their GitLab data. But look, no 2 organizations or even 2 teams in an organization work the same way. That's why we built the AI catalog, a central place to create, publish and manage custom agents that reflect your workflows, your standards, your domain knowledge. This is the same platform GitLab uses internally to build the agents for you. So AI doesn't exist as a side experiment anymore. It becomes operational, shareable and governed across your organization. Now some of you are already using tools like Claude Code from Anthropic or Codex from OpenAI. That's why we have integrated them as external agents. These are best-in-class agents now tightly integrated in GitLab without you having to give up on governance or auditability. Next is the control layer. We extended GitLab's existing control plane, Git APIs and Webhooks into a control plane for agents. This includes Agentic flows, which lets you chain agents together to automate real work. We've shipped prebuilt foundational flows for things like creating a merge request from issues, fixing broken pipelines or even migrating pipelines, automating code reviews, amongst others. And planned for a future release, custom flows for you to author workflows yourself plus event triggers to invoke flows in the background, not just when someone clicks a button. That's how manual effort turns into repeatable processes. Now interoperability is also essential here. That's why GitLab has native support for model context protocol. With our MCP clients, agents in GitLab have a governed way to pull context from tools you already use like Jira, Confluence and Slack, amongst others. With the planned general availability of our MCP server, you can extend capabilities and context in GitLab to your favorite tools. Our goal here is to make GitLab a part of your broader AI ecosystem bidirectionally and always with governance. All of this is powered by unified context from the data layer. Every repo, every merge request, every pipeline, every security signal and all the related metadata is now accessible to agents with enterprise-grade security. The new agent sessions is a complete audit trail of every AI interaction. You can see what the agent planned, what decisions it made and what actions it took. So AI and GitLab is observable, inspectable and compliance ready. To go one step further, we are building the GitLab Knowledge Graph with planned availability this year, indexing repositories and related metadata, producing a semantic search API. You see agents operate under constraints of the context window and often have latency. In our early testing, with agents working alongside Knowledge Graph, we found that agents are responding notably faster and with increased accuracy. Behind the scenes, we're also upgrading the underlying data stores for broader insights and higher performance. That's what powers our engineering intelligence dashboard designed to help leaders see where teams slow down, where quality is improving and where AI is actually helping. Let me show you a preview. I'm sure you can see a big difference right away. Instead of looking at metrics project by project, you can now see aggregated insights across the entire organization. 234 groups and about 1,500 projects. Just in the last 30 days, 6,500 users and their agents have opened about 16,000 issues, merged 470,000 MRs and had over 600,000 pipeline runs. With your intelligence dashboard, you don't need to piece together different views to know if you're getting better your craft. You can get a snapshot of DORA metrics and the trends in the same place and see the AI adoption across all your teams. And what impact is that having to your DORA metrics. That's really useful to measure how the AI rollouts are going and resulting in business impact. You can dive deeper to compare projects for each metric. For example, let's take a look at the 10 projects that are scoring lowest on lead time for changes and see what's going on here. This table shows me that the number of pipeline runs is declining. But more notably, the failures are surging. That's not good. Because of the unified context you have in GitLab, you can dive in further by tapping over to projects. It looks like the issue with the payment service is the one that's most acute. From here, you have a connected workflow to see the details before reaching out to the team manager and asking them how you can help. Now I'm familiar with this dashboard. You may not be. You could have also done the same exact thing in a different way by simply asking the data analyst agent to do this work for you. What you saw here in this demo is the power of GitLab's next-generation data layer, bringing unified context across groups and projects to unlock unique intelligence for your software life cycle. Now let's talk about the DevOps and security tooling that sits on top of the new Agentic core. GitLab's strength has always been to stitch together the end-to-end software engineering process, replacing the friction of disconnected point solution. Taking into account feedback from all of you, our customers, we are now going deeper in several areas. For instance, with CI/CD, over 550 million pipelines are created inside GitLab every year. But building the right pipeline is hard. Fixing the failing one, well, that's a huge time sick. That's why we plan to introduce an AI-first CI/CD visual builder, enabling you to create and optimize complex pipelines without wrestling with YAML. Next, artifact management. I hear this constantly. Why are we managing artifacts somewhere else when all my repos and my CI workflows are in GitLab. It makes sense. especially since legacy vendors are sunsetting self-managed options and forcing migrations to the cloud that increase your costs substantially. With built-in artifact management planned for this year, you will be able to host your artifacts where you deploy GitLab. When artifacts live alongside your code and your pipelines, you get one view from commit to deployment, no off handoffs, no sync failures. Next, software supply chain security. Especially with AI-generated code, software teams are increasingly asking, what components are we using? Can we trust them? How do we prove it to auditors? Well, that's why this year, we plan to add a new software supply chain security module, building trust within your software life cycle to show you where code came from, highlight what's inside it and help block anything risky or unsigned at the artifact registry before it ever reaches production. Related, secrets management, API keys, certificates, passwords, you need them encrypted, rotatable and visible. While you can manage secrets outside GitLab, having it integrated is just much more efficient and helps enforce stronger compliance standards. With the planned introduction of GitLab's Secret Manager, you'll be able to manage your secrets directly within GitLab, where your code and policy already lives. So that's where we are deepening the tooling you have from GitLab today. Before we continue, here's a quick video on our value in enabling automated security and compliance across your software life cycle. We'll be right back after that. [Presentation]

Now let's talk about enterprise guardrails because none of this matters if you can't deploy GitLab on your terms and align with the regulations and compliance requirements that are only getting harder to satisfy. Diving into deployment flexibility, I've heard horror stories. Vendors are sunsetting on-prem tools, forcing cloud migrations, changing pricing models. Well, GitLab is different. We believe you should choose how and where you run GitLab. GitLab self-managed. So you are always in full control with your infrastructure. GitLab.com, our multi-tenant SaaS offering where you can get started instantly, GitLab Dedicated, single-tenant SaaS with data isolation and GitLab Dedicated for government, FedRAMP authorized for critical national infrastructure. The same flexibility you've had with GitLab deployments now also extends to AI, giving you the option to bring your own models. These models can be on your infrastructure, air gapped if needed, so inference stays in your environment or through the AI gateway or model provider of your choice, so you're not taking on new risk or forced to adjust your compliance posture. So that's intelligent orchestration with the Agentic Core, unified DevOps and security tooling and enterprise guardrails, working together to help you create your continuous innovation loops across your organization. For over a decade, we have consistently delivered new capabilities across our unified platform with more than 180 monthly releases in a row. That commitment continues with intelligent orchestration. But I don't want you to take my word for it. I want you to try it. Starting today, every premium and ultimate customer gets monthly GitLab credits, $12 and $24 in credits, respectively, for every user at no additional cost. And if you're not a customer yet, start your trial at gitlab.com. And for everyone in our community, we're kicking off a virtual hackathon. A huge thanks to our partners at Anthropic and Google for joining us. Build custom agents, build agentic workflows, the best projects earn a permanent spot in our AI catalog, plus continued support from our team. We can't wait to see what you build. And now to show you how you can scale adoption of intelligent orchestration in your organization within your time lines, let me hand it back to Sherrod. She's also got a special guest you won't want to miss. Thank you.

For those of you who are inspired by Southwest's journey with GitLab, I also want to take a moment to dive into GitLab's value acceleration services, how they work and how customers get started. We don't just sell you software, we partner with you to deliver measurable outcomes, reducing time to value from months, all the way down to weeks. For example, as part of your AI modernization journey, we start by identifying your highest value use cases for GitLab Duo agent platform. Our forward deployed engineers become part of your teams to build custom agents aligned with your engineering practices and compliance requirements. Hands-on training gets your teams creating agents, configuring workflows and managing the platform independently and governance frameworks that we build together ensure you can scale agentic AI safely across your organization. We integrate with your existing systems. We customize for your compliance needs. We enable your teams to accelerate their innovation cycles. With GitLab value acceleration services, you can choose the level of support that fits your time line, self-serve resources, guided workshops, all the way through to full implementations with engineers embedded in your teams. Whether you need a rapid 90-day outcome for specific use cases or a multi-quarter transformation that changes how you deliver software, we're here for it. As we look to scale our ability to solve challenges for all our customers worldwide, we rely on our partner ecosystem. Built on 4 pillars, it is designed to give you choice, flexibility and extensibility. Starting with technology alliances where we work with independent software vendors who integrate with GitLab, giving you the ability to build the stack that fits your unique requirements. Next, cloud provider partnerships. GitLab is cloud agnostic, deploy on AWS, Google Cloud, Microsoft Azure, Oracle Cloud Infrastructure or your own infrastructure, choose based on cost, compliance, geography or existing relationships. Next, our partnership with AI providers, including Anthropic, OpenAI, Google and AWS. Some customers choose model by task, Claude Code for coding, others for different tasks, all tightly integrated with GitLab with embedded security and subscriptions. And finally, GitLab's open architecture. With our open source core, GitLab's code base is transparent, auditable and yours to deploy, customize and extend. This unique approach powers a thriving community of contributors and integrations, giving you the freedom to adapt GitLab in your own way. One of our key partners in bringing GitLab to market at enterprise scale is Oracle. Together, we are collaborating to expand customer choice with streamlined GitLab deployments on Oracle Cloud Infrastructure, OCI. This partnership brings GitLab together with OCI's industry-leading cloud economics, extensive global footprint and flexible deployment options, including specialized environments for regulated industries. Please join me in welcoming Victor Restrepo, Group Vice President for North America OCI Engineering, to discuss this exciting collaboration. Victor, thank you so much for joining us today.

Thanks for having me, Sherrod. Great to be here.

It's probably well known by now that both organizations have a strong commitment to open source technology and customer choice. What else do you think is driving OCI's focus on DevSecOps partnerships? And what other factors played a role in your decision to partner with GitLab?

At Oracle, we focus our cloud strategy around delivering open source technologies as first-party services. And GitLab exemplifies exactly what we mean by that. We're seeing that companies can't afford fragmented tool chains or security as an afterthought anymore. They need integrated platforms that actually move at the speed that the business requires while maintaining enterprise-grade security and compliance. So what this means for customers is dramatically simplified modernization journey. Our joint teams work directly with customers to understand their specific requirements, whether that's they're trying to drive cost optimization, regulatory compliance requirements or scale requirements in their business. So instead of having to piece together the infrastructure, the platform and the tooling separately, they get a pre-validated enterprise-ready solution that is architected together. The real value is that you get the best of both worlds. So GitLab gives you all of the DevSecOps features that you need and then OCI brings the cloud economics that make it possible to fund and scale the deployments.

Awesome. That is such a powerful combination. It's clear that many organizations could benefit from the freedom of choice that you're highlighting here. I'm glad you touched on cloud economics. As we all know, cost optimization is always top of mind for every CTO and engineering leader. So taking into account OCI's global footprint, how do you see this partnership influencing the way organizations think about scaling their GitLab deployments?

That's such a powerful and top-of-mind question that everybody has. So cloud economics is absolutely critical. And it's an area where OCI has a distinct advantage. So in building our cloud, we made several key architectural decisions that enable us to deliver stronger, more modern platform for customers at a lower cost than other hyperscalers. So when our customers can reduce their infrastructure spend by 40% to 50%, they could actually free up the budget for the development teams to drive real business outcomes and transformations. So as an example, you could afford to give developers access to more resources such as running a more comprehensive security scans or maintaining more environments for testing and staging. But it's not just about lowering the cost. It's about having predictable costs. Our global footprint and price parity gives customers the flexibility to deploy GitLab closer to where their development teams are as well as where the data resides. And that gives us the opportunity to help reduce the latency and improve developer productivity. What this means is that developers aren't just waiting for bills or deployments that could actually translate that directly into business value.

I totally agree. Thank you for sharing your strategy there. So to close our discussion, speaking of developer productivity, as we look at GitLab's Duo Agent platform and OCI's AI services, what opportunities do you see for integration that can benefit our joint customers?

So this is where things get really exciting. So what GitLab's Duo Agent platform represents the future of software development, intelligent agents that can actually automate complex workloads across the entire application life cycle. OCI's AI capabilities enhance the Duo Agent platform by delivering enterprise AI infrastructure for organizations so that they could accelerate their code creation, testing and remediation while keeping the proprietary source code private and governed. So looking ahead, we see opportunities for GitLab to leverage various OCI deployment models such as government cloud, dedicated cloud region to better support our customers in regulated and government industries. This partnership positions our joint customers to be leaders in that AI-driven future. And frankly, I think we're just scratching the surface of what's possible when you combine GitLab's platform approach with OCI's infrastructure and AI capabilities.

I couldn't agree more. This has been an incredibly exciting partnership for GitLab, and I'm looking forward to serving many of our joint customers in the days ahead. Thank you again, Victor, for joining Transcend.

Absolutely. It's been a pleasure to bring this collaboration to life, and thank you for having me in the room.

To close our show, let me hand it back over to Bill.

Thank you, Sherrod. To close, I want to express my heartfelt thanks to the entire GitLab team for making this moment possible. Your dedication, passion and commitment to our customers and the entire community is truly inspiring. Thank you to Grant Morris from Southwest Airlines and Victor Restrepo from Oracle for sharing your insights and experiences with our community. And thank you all who are attending Transcend today virtually or in person. You've seen the problem, engineers drowning in ceremony, waiting in queues, fighting systems and triaging noise. And you've seen the solution, agentic AI across the entire software life cycle, shifting developers from drudgery to high-value work, replacing status theater with automated visibility and collapsing the friction between idea and production. Now you've seen how GitLab delivers it, your system of record with complete life cycle context and interloop architecture that optimizes your workflows and enterprise-grade protection within your guardrails. The future of software development isn't just faster coding, it's faster everything from idea to production with AI agents as trusted collaborators at every step. The question isn't whether agentic AI will transform software development. The question is whether you're in position to harness it. We've spent a decade building GitLab. Now there's an agentic layer at the heart of it, bring everything you love about GitLab forward into the AI era. As we close our show, here's a look at the new GitLab. Let's roll the video.