[{"data":1,"prerenderedAt":807},["ShallowReactive",2],{"/en-us/blog/three-faces-of-user-calls":3,"navigation-en-us":38,"banner-en-us":448,"footer-en-us":458,"blog-post-authors-en-us-Viktor Nagy":698,"blog-related-posts-en-us-three-faces-of-user-calls":712,"blog-promotions-en-us":744,"next-steps-en-us":797},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":12,"meta":27,"navigation":28,"path":29,"publishedDate":20,"seo":30,"stem":34,"tagSlugs":35,"__hash__":37},"blogPosts/en-us/blog/three-faces-of-user-calls.yml","Three Faces Of User Calls",[7],"viktor-nagy",null,"product",{"slug":11,"featured":12,"template":13},"three-faces-of-user-calls",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How product managers can get more out of user calls","There are 3 types of user calls. Here's how GitLab product managers approach them and how we leverage our transparency value to better understand our users.",[18],"Viktor Nagy","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749682372/Blog/Hero%20Images/michal-czyz-ALM7RNZuDH8-unsplash.jpg","2022-07-20","\n\nOne of the core jobs of product managers is to speak with users to better understand their needs, pain points and the context in which they operate and use our products. But not all user calls are the same. \n\nThere are 3 prominent types of user calls:\n\n- Discovery or problem validation calls\n- Roadmap discussions\n- Solution validation calls\n\nHere's an in-depth look at how we approach the three types of user calls at GitLab.\n\n## Discovery calls\n\nDiscovery or problem validation calls are product managers' most crucial conversations with users. Discovery calls are typically set up to learn about our users in a targeted way. These calls help build a better understanding of users' pain points. \n\nFor discovery, we need a recipe for repeatable, comparable user calls. For this reason, we should create an interview script and follow that script on all the user calls. This does not mean these calls are robotic and devoid of improvisation, not at all! The script should provide the backbone of the discussions. We can adjust it either during the call or in advance based on prior knowledge about the user. Good discovery calls typically take the form of a deep-dive conversation: we know the script by heart and can run back and forth around it, always asking the questions that fit the conversation. \n\nFinding the right users is one of the most challenging parts of discovery calls. Thankfully, with GitLab, this is relatively easy. We can always reach out to the most active users on issues and invite them to a call. Another technique I employ is to find users in the [Cloud Native Computing Foundation](https://www.cncf.io) and Kubernetes communities' Slack channels and articles on [Medium](https://medium.com). This way, I can also find non-GitLab users, a set of people likely more valuable to interview than existing users. Finally, we can recruit users with the support of the account managers. They are always helpful in connecting PMs with users. Asking the users about their needs shows them that we genuinely care about them.\n\nThere are at least two distinct discovery calls: PM-led or UX-led. UX research typically works on projects with a strict scope. For PM-driven calls, a great framework is [\"Continuous discovery\" calls by Teresa Torres](https://www.producttalk.org/continuous-discovery/). With continuous discovery, we build a deep understanding of our users and get well-understood opportunities. The technique allows us to get a broad view and to dive deep into specific aspects of our problem space when needed.\n\n## Roadmap discussions\n\nRoadmap discussion calls are typically initiated by sales or account management teams. Product managers are asked to join the prospect/customer call to strengthen our positions and show how much we care for the customer. \n\nTo prepare for roadmap discussions, PMs should have an effective way to present the roadmap. This typically happens in the form of slides. A diligent PM might even prepare something specifically for the client.\n\nDuring these calls, the user/customer/prospect will typically ask the questions, and the PMs respond. Our role in these calls is to represent the truth. We might be tempted to paint a rosier picture about the current or expected state of the product than is actually true, and we should avoid making time-bound promises.\n\nWhat are the expected outcomes of roadmap discussions? They can help strengthen our position with the user. Remember that these calls primarily cater to our customers/users and customer-facing teams. As such, they are unlikely to provide deep learning about our users. \n\nIf we approach these calls with the intention to prove that our roadmap is correct, we will likely fall victim to both response and confirmation biases. There are techniques to validate a roadmap, but they are more aligned with problem validation than roadmap discussion calls. For example, UX researchers should be able to help validate a roadmap as a UX research project.\n\n## Solution validation calls\n\nLast but not least, we have solution validation calls. These calls serve our learning but are way more focused than discovery calls. Solution validation calls require some form of a prototype for a specific problem we want to test and get feedback on from our users.\n\nAt GitLab, the prototypes are typically built by product design or engineering. The product manager might miss some of these calls in an empowered and autonomous team. But, as these calls are great learning experiences, we should aim to be there to support and learn if we can.\n\nA solution validation call might be started with a concise roadmap discussion. Unlike in sales calls, our aim is not to influence the user but to set the scene for solution validation. The central part of the call should be around the proposed solution. We should provide the least amount of guidance to our users since there are no humans available to direct our users when they are working with the actual product. If much guidance is required, that is a sign that we might want to rethink our UX approach.\n\nFinding suitable interview candidates for a solution validation call might be tricky. For GitLab, we often use the shortcut of inviting users based on their activity on relevant issues. Sometimes, when our issues provide enough context, we might get some solution validation asynchronously as users give their feedback directly in the issue.\n\n## How many calls?\n\nHow often does a good PM have all these calls? For discovery calls, I aim to have 3 calls per week. Above this, I don’t mind taking 1 sales call. While I prefer the product designer to run solution validation calls, I try to participate there too. Not every solution requires dedicated validation, so having a target number for solution validation calls is unrealistic. The better the discovery calls are, the fewer solution validation calls you might need. Still, even the best discovery cannot and should not answer all the questions of a solution validation. Often there are different (and totally valid) approaches to the same problem, and we need to pick the one that is the easiest for users to understand.\n\nI think we need to speak to our users every day. Working at GitLab, sometimes this might take the form of issue comments, but face-to-face calls are a must. In any case, during these discussions we should aim to learn from our users, not just answer their questions. A handy question in issues is to ask for more context from our users. The response might highlight unknown use cases or edge cases we missed previously.\n\n## Take the calls\n\nIt is helpful to remember all the user call types we practice as PMs. As mentioned, I think the most crucial user calls for PMs are the discovery calls. If we don’t make discovery calls, nobody will; also, PMs might not be needed in the other calls. That said, a product manager's job is to also help the business be viable. So we should be able to support sales and always have a deck ready for roadmap calls. 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18.11: Budget guardrails for GitLab Credits","Learn how new spending caps and per-user credit limits give organizations the budget guardrails to scale GitLab Duo Agent Platform.",[718],"Bryan Rothwell","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776259080/cakqnwo5ecp255lo8lzo.png","2026-04-16","Teams using GitLab Duo Agent Platform with on-demand GitLab Credits are shipping faster, catching bugs earlier, and automating tasks that used to take entire sprints. But as adoption grows, so does oversight from finance, procurement, and platform teams to prove that AI spending is bounded, predictable, and controllable.\n\nOne of the greatest barriers to broader AI adoption isn't skepticism about the technology. It's uncertainty about managing spend. Without budget caps, a busy month could produce unexpected expenses. Without per-user limits, a handful of power users could burn through the team's credits before the month is over. And without either, engineering leaders who want to expand their use of agentic AI for software development have to jump through more hoops for budget approval.\n\nSince its [general availability](https://about.gitlab.com/blog/gitlab-duo-agent-platform-is-generally-available/), GitLab Duo Agent Platform has provided usage governance and visibility. With GitLab 18.11, we're introducing usage controls for [GitLab Credits](https://about.gitlab.com/blog/introducing-gitlab-credits/): spending caps and budget guardrails that give your organization even more control and transparency over how credits are consumed.\n\n## Managing GitLab Credits\n\nGitLab 18.11 adds three layers of control over GitLab Credits consumption: a subscription-level spending cap, per-user credit limits, and visibility into cap status and enforcement.\n\n### Subscription-level spending cap\n\nBilling account managers can now set a hard monthly ceiling for on-demand GitLab Credits consumption for their entire subscription.\n\nHere's how it works:\n\n* **Set a cap** in the `Customers Portal` under your subscription's GitLab Credits settings.  \n* **Enforce spend limits automatically.**  When on-demand usage reaches the cap, DAP access is paused for all users on that subscription until the next monthly period begins.  \n* **Make adjustments as you go.** Raise or disable the cap mid-month to restore access.\n\nThe cap resets each monthly period and your configured limit carries forward unless you change it. Because usage data is synchronized periodically rather than in real time, a small amount of additional usage may occur after the cap is reached before enforcement takes effect. See the [GitLab Credits documentation](https://docs.gitlab.com/subscriptions/gitlab_credits/) for details.\n\n### User-level spending caps\n\nNot every user consumes credits at the same rate, and that's expected. But when one or two power users account for a disproportionate share of the pool, the rest of the team can lose access before the month is over.\n\nPer-user credit caps prevent any single user from consuming more than their fair share:\n\n* **Flat per-user cap.** Set a uniform credit limit that applies equally to every user on the subscription through the GitLab GraphQL API. Unlike the subscription-level cap, the per-user cap applies to a user's total consumption across all credit sources.  \n* **Custom per-user overrides.** For organizations that need differentiated limits, you can set individual credit caps for specific users through the GraphQL API. For example, you could give your staff engineers a higher allocation while applying a standard limit to the broader team.  \n* **Individual enforcement.** When a user reaches their cap, they retain full access to GitLab. Only their Duo Agent Platform credit usage is paused until the next billing cycle. Everyone else keeps working uninterrupted until they hit their own limit or the subscription-level cap is reached, whichever comes first.\n\n### Visibility and notifications\n\nWhen a subscription-level cap is reached, GitLab sends an email notification to billing account managers so they can take action: raise the cap, wait for the next period, or redistribute credits.\n\nWithin GitLab, group owners (GitLab.com) and instance administrators (Self-Managed) can view which users have been blocked due to reaching their per-user cap and restore access by adjusting the cap through the GraphQL API. \n\n## How budget guardrails help organizations scale AI usage\n\nGuardrails are essential as organizations ramp up their AI adoption. Here's why:\n\n### Predictable AI budgets\n\nUsage controls for GitLab Duo Agent Platform turn AI into a bounded, predictable budget item using on-demand GitLab Credits. That makes it easier to deploy agents across the software development lifecycle and get sign-off from finance, justify renewals, and plan quarterly spend.\n\n### Governance and chargeback\n\nLarge organizations often need to align AI consumption with internal budgets, cost centers, or departmental policies. Per-user caps give platform teams a straightforward mechanism to allocate credits fairly and track consumption at the individual level. The API import options make it practical to manage caps at enterprise scale. Combined with per-user usage data from the GitLab Credits dashboard, organizations can track consumption patterns to inform their own internal chargeback or budget allocation processes.\n\n### Confidence to scale\n\nMany customers start GitLab Duo Agent Platform with a small pilot group. Usage controls remove risks associated with expanding that pilot across the organization. You can roll out Duo Agent Platform to hundreds or thousands of developers knowing there's a hard ceiling protecting your budget. If usage grows faster than expected, you'll hit the cap, not an unexpected invoice.\n\n## Addressing the seat-based and visibility conundrum\n\nMany AI coding tools take a seat-based approach to cost management. You buy a fixed number of seats at a flat per-user price, and that's your budget. It's simple, but rigid. You pay the same whether a developer uses the tool ten times a day or never touches it. And as vendors introduce premium models and usage-based overages on top of seat pricing, the cost predictability that seat-based licensing promised starts to erode.\n\n\nGitLab takes a different approach. Usage-based pricing with hard caps and a single governance dashboard. You get the flexibility of paying for what your teams actually use, with the budget predictability of enforced spending limits.\n\n## Real-world usage controls\n\n**One example is a mid-size SaaS customer that wants to protect their monthly budget.** A 200-person engineering organization sets a subscription-level cap equal to their expected on-demand usage. Their VP of Engineering can confidently tell finance that GitLab Duo Agent Platform spend will never exceed the approved amount, even as they onboard new teams. If they approach the cap mid-month, the billing account manager gets a notification and can decide whether to raise the limit or wait for the next period.\n\n**At GitLab, we also work with large enterprises that want to keep usage fair across teams.** A global financial services company with 2,000 developers uses per-user caps to ensure equitable access. Staff engineers working on complex refactoring projects get a higher individual allocation via API, while most developers receive a standard flat cap. No single user can exhaust the pool, and the platform team uses the per-user usage data in the GitLab Credits dashboard to track consumption patterns and inform quarterly budget planning.\n\n## Getting started\n\nUsage controls are available for both GitLab.com and Self-Managed customers running GitLab 18.11. Different controls are configured in different places depending on the scope and your role.\n\n**Subscription-level cap**\n\nBilling account managers set the subscription-level on-demand cap in the Customers Portal:\n\n1. Sign in to the `Customers Portal`.  \n2. On your subscription card, navigate to **GitLab Credits** settings.  \n3. Enable the monthly on-demand credits cap and enter your desired limit.\n\n**Flat per-user cap**\n\nThe flat per-user cap can be set through the GitLab GraphQL API by namespace owners (GitLab.com) or instance administrators (Self-Managed). Check the [GitLab Credits documentation](https://docs.gitlab.com/subscriptions/gitlab_credits/) for the latest on available configuration surfaces.\n\n**Custom per-user overrides**\n\nFor differentiated limits, namespace owners (GitLab.com) and instance administrators (Self-Managed) can set individual caps programmatically. This is useful for automation and infrastructure-as-code workflows.\n\n**Monitor usage and cap status**\n\n* **Customers Portal:** View detailed usage and cap status.  \n* **GitLab.com:** Group owners can view blocked users under **Settings > GitLab Credits**.  \n* **Self-Managed:** Instance administrators can view cap status and blocked users under **Admin > GitLab Credits**.\n\n## GitLab Duo Agent Platform is ready to scale\n\nUsage controls are available now in GitLab 18.11. If you've been waiting for the right guardrails before expanding GitLab Duo Agent Platform across your organization, this is your moment. Set your caps, roll out Duo Agent Platform to more teams, and start shipping faster!\n\n> [Learn more about GitLab Credits and usage controls](https://docs.gitlab.com/subscriptions/gitlab_credits/).",[9,723,724],"AI/ML","news",{"featured":12,"template":13,"slug":726},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"content":728,"config":731},{"title":729,"heroImage":719,"description":730,"date":720,"category":9},"GitLab 18.11 release","This release includes Agentic SAST Vulnerability Resolution, Data Analyst Foundational Agent, CI Expert Agent, and more.",{"featured":12,"template":13,"externalUrl":732},"https://docs.gitlab.com/releases/18/gitlab-18-11-released/",{"content":734,"config":742},{"title":735,"description":736,"authors":737,"heroImage":719,"date":720,"body":739,"category":9,"tags":740},"GitLab 18.11: CI Expert and Data Analyst AI agents target development gaps","Set up CI and query your software development lifecycle data with two new GitLab Duo Agent Platform foundational agents available in GitLab 18.11.",[738],"Corinne Dent","AI-generated code moves faster than the systems around it can keep up with. More code means more merge requests queued, more pipelines to configure, more questions about delivery that nobody has time to answer — and most of the tooling teams rely on wasn't built for this pace.\n\nIn GitLab 18.11, two new foundational agents for Duo Agent Platform address specific gaps in the development lifecycle that AI has largely left untouched:\n* CI Expert Agent (now in beta) focuses on the gap between writing code and getting it into a running pipeline\n* Data Analyst Agent (now generally available) focuses on the gap between shipping code and being able to answer basic questions about how that delivery is actually going.\n\n\nThese are problem areas that couldn't be solved by a general-purpose assistant. A tool running outside GitLab can generate a YAML file or answer a question, but it has no awareness of how your pipelines have historically performed, where failures cluster, or what your actual MR cycle times look like. That context lives in GitLab. These agents do too.\n## Fast CI setup with CI Expert Agent\n\nAI has made it easier than ever to write code. Getting that code into a running pipeline is still something most teams do days, or weeks, later — if at all. The blank-page problem isn't in the editor anymore. The blank page is now in `.gitlab-ci.yml`.\n\nDevelopers who have never configured CI don't know what language detection looks like in YAML, what their test commands should be, or how to validate the result before pushing. Teams either copy a config from a previous project that may not fit, stitch together examples from documentation, or wait for the one person who's done it before. If that person isn't available, CI becomes the thing you'll \"get to later.\" Later becomes never.\n\nWhen CI never happens, the impact shows up everywhere else. Changes ship without a reliable safety net, regressions surface in production instead of in pipelines, and work piles up in bigger, riskier batches because no one wants to be the person who “breaks the build.” Over time, teams normalize working in the dark, often relying on undocumented institutional knowledge and ad-hoc testing, instead of having a fast, predictable feedback loop baked into every change.\n\nCI Expert Agent, now available in beta, removes that friction. It inspects your repository, identifies your language and framework, and proposes a working build and test pipeline tailored to what's actually there — then explains every decision in plain language. The target: a running pipeline in minutes, with no YAML written by hand.\n\nWhat CI Expert Agent does:\n\n* Repo-aware pipeline generation detects language, framework, and test setup \n* Generates valid, runnable build and test configurations   \n* Guided first-pipeline flow with plain-language explanation of each step in Agentic Chat  \n* Native GitLab CI semantics with no config translation required\n\nBecause it runs inside GitLab and sees real pipeline behavior over time, each improvement can build on how teams actually work, not just on static examples.\n\u003Ciframe src=\"https://player.vimeo.com/video/1183458036?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"CI/CD Expert Agent\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cbr>\u003C/br>\n\nCI Expert Agent is available on GitLab.com, Self-Managed, Dedicated; Free, Premium, Ultimate Editions with Duo Agent Platform enabled.\n\n## Query GitLab data in plain language with Data Analyst Agent\n\nAI has sped up how teams ship. Answering basic questions about how that work is going has gotten harder, not easier.\n\nHow long are MRs sitting in review? Which pipelines are slowing teams down? Are deployment targets actually being hit? These questions used to be answerable by glancing at a dashboard. Now, with more code, more teams, and more complexity, the data exists — it's in GitLab — but accessing it still means waiting on an analytics team, filing a dashboard request, or learning GLQL.\n\nData Analyst Agent targets that gap. Ask a natural-language question and get an instant visualization in Agentic Chat. No query language, no dashboard request, no waiting for the answers to be assembled by someone else.\n\nFor example, the agent can answer questions about the following topics for these roles:\n\n* Engineering managers: MR cycle time, throughput by project, where reviews get stuck  \n* Developers: Contribution patterns, flaky tests blocking their MRs, pipeline speed trends  \n* DevOps and platform engineers: Pipeline success/failure rates, runner utilization, deployment frequency  \n* Engineering leadership: Cross-portfolio deployment frequency, project health metrics, lead time comparisons\n\nNow generally available in 18.11, the agent covers MRs, issues, projects, pipelines, and jobs — full software development lifecycle coverage, expanded from the beta scope. Because Data Analyst Agent queries what's already in GitLab, the context is always current, and there's no pipeline to maintain or third-party tool to keep synchronized. Generated GitLab Query Language queries can be copied and used anywhere GitLab Flavored Markdown is supported, with direct export to work items and dashboards on the roadmap.\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1183094817?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Data Analyst agent demo\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cbr>\u003C/br>\n\nData Analyst Agent is available on GitLab.com, Self-Managed, Dedicated; Free, Premium and Ultimate Edition with Duo Agent Platform enabled.\n\n## One platform, connected context\n\nBoth agents run inside GitLab, with access to the code, pipelines, issues, and merge requests already there. That's what separates platform-native AI from a disconnected assistant: the context is always current, and it only gets more useful over time. CI Expert Agent and Data Analyst Agent represent two concrete steps toward a platform where AI doesn't just help you write code faster; it helps you understand, ship, and maintain what gets built.\n\n> [Start a free trial of GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo/) to experience these foundational AI agents.",[723,741,9],"features",{"featured":28,"template":13,"slug":743},"ci-expert-and-data-analyst-ai-agents-target-development-gaps",{"promotions":745},[746,760,771,783],{"id":747,"categories":748,"header":750,"text":751,"button":752,"image":757},"ai-modernization",[749],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":753,"config":754},"Get your AI maturity score",{"href":755,"dataGaName":756,"dataGaLocation":242},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":758},{"src":759},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":761,"categories":762,"header":763,"text":751,"button":764,"image":768},"devops-modernization",[9,566],"Are you just managing tools or shipping innovation?",{"text":765,"config":766},"Get your DevOps maturity score",{"href":767,"dataGaName":756,"dataGaLocation":242},"/assessments/devops-modernization-assessment/",{"config":769},{"src":770},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":772,"categories":773,"header":775,"text":751,"button":776,"image":780},"security-modernization",[774],"security","Are you trading speed for security?",{"text":777,"config":778},"Get your security maturity score",{"href":779,"dataGaName":756,"dataGaLocation":242},"/assessments/security-modernization-assessment/",{"config":781},{"src":782},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":784,"paths":785,"header":788,"text":789,"button":790,"image":795},"github-azure-migration",[786,787],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":791,"config":792},"See how GitLab compares to GitHub",{"href":793,"dataGaName":794,"dataGaLocation":242},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":796},{"src":770},{"header":798,"blurb":799,"button":800,"secondaryButton":805},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":801,"config":802},"Get your free trial",{"href":803,"dataGaName":49,"dataGaLocation":804},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":504,"config":806},{"href":53,"dataGaName":54,"dataGaLocation":804},1776452978331]