[{"data":1,"prerenderedAt":824},["ShallowReactive",2],{"/en-us/blog/three-faces-of-user-calls":3,"navigation-en-us":40,"banner-en-us":461,"footer-en-us":471,"blog-post-authors-en-us-Viktor Nagy":711,"blog-related-posts-en-us-three-faces-of-user-calls":726,"blog-promotions-en-us":761,"next-steps-en-us":814},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":24,"description":17,"extension":25,"externalUrl":26,"featured":15,"heroImage":19,"isFeatured":15,"meta":27,"navigation":28,"path":29,"publishedDate":24,"rawbody":30,"seo":31,"slug":14,"stem":35,"tagSlugs":36,"tags":38,"template":13,"updatedDate":26,"__hash__":39},"blogPosts/en-us/blog/three-faces-of-user-calls.md","How product managers can get more out of user calls",[7],"viktor-nagy",[9],"Viktor Nagy","One 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. Lastly, we should work continuously with our team on solution validation so that everyone is confident in our solution.","product",{"template":13,"slug":14,"featured":15},"BlogPost","three-faces-of-user-calls",false,{"title":5,"description":17,"authors":18,"heroImage":19,"tags":20,"category":11,"date":24,"body":10},"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.",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749682372/Blog/Hero%20Images/michal-czyz-ALM7RNZuDH8-unsplash.jpg",[21,22,23],"DevOps","customers","contributors","2022-07-20","md",null,{},true,"/en-us/blog/three-faces-of-user-calls","---\nseo:\n  title: How product managers can get more out of user calls\n  description: >-\n    There are 3 types of user calls. Here's how GitLab product managers approach\n    them and how we leverage our transparency value to better understand our\n    users.\n  ogTitle: How product managers can get more out of user calls\n  ogDescription: >-\n    There are 3 types of user calls. Here's how GitLab product managers approach\n    them and how we leverage our transparency value to better understand our\n    users.\n  noIndex: false\n  ogImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1749682372/Blog/Hero%20Images/michal-czyz-ALM7RNZuDH8-unsplash.jpg\n  ogUrl: https://about.gitlab.com/blog/three-faces-of-user-calls\n  ogSiteName: https://about.gitlab.com\n  ogType: article\n  canonicalUrls: https://about.gitlab.com/blog/three-faces-of-user-calls\ntitle: How product managers can get more out of user calls\ndescription: 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.\nauthors:\n  - Viktor Nagy\nheroImage: https://res.cloudinary.com/about-gitlab-com/image/upload/v1749682372/Blog/Hero%20Images/michal-czyz-ALM7RNZuDH8-unsplash.jpg\ntags:\n  - DevOps\n  - customers\n  - contributors\ncategory: product\ndate: '2022-07-20'\nslug: three-faces-of-user-calls\nfeatured: false\ntemplate: BlogPost\n---\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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foundation","Pair GitLab Duo Agent Platform with Amazon Bedrock for agentic software development and orchestration.","If your team runs GitLab and has a strong AWS practice, a new combination of Duo Agent Platform and Amazon Bedrock is just for you. The model is simple: GitLab acts as your orchestration layer to help accelerate your entire software lifecycle with agentic AI, and Bedrock is designed to provide a secure, compliant foundation model layer with AI inference behind the scenes.\n\nGitLab Duo Agent Platform enables you to handle planning, merge pipelines, security scanning, vulnerability remediation, and more as part of your GitLab workflows, while the GitLab AI Gateway routes model calls to Bedrock (or GitLab-managed Bedrock-backed endpoints, depending on your setup). That means you can build on the identity and access management (IAM) policies, virtual private cloud (VPC) boundaries, regional controls, and cloud spend commitments you already have in AWS.\n\nIf you already use Amazon Bedrock and want AI to help inside the work you already do in GitLab, not in yet another standalone chat tool, this is the pairing for you.\n\n\nIn this article, we look at the real problem many teams face today: AI is fragmented, data paths are fuzzy, and Bedrock investment gets underused when AI sits outside the software development lifecycle. Then we break down your deployment options for GitLab Duo Agent Platform:\n\n* Integrated with self-hosted models on Amazon Bedrock for GitLab Self-Managed deployments and self-hosted AI gateway   \n* Integrated with GitLab-operated models on Amazon Bedrock (with GitLab-owned keys) for GitLab Self-Managed deployments and GitLab-hosted AI gateway  \n* Integrated with GitLab-operated models on Amazon Bedrock (with GitLab-owned keys) for GitLab.com instances and GitLab-hosted AI gateway\n\nWe wrap with a summary on how this approach helps avoid shadow AI and point-tool sprawl without creating a parallel tech stack for AI tooling.\n\n## AI everywhere, control nowhere\n\nSomewhere in your company right now, software teams might be using an AI tool that your security team hasn't approved. Prompt data might be leaving your environment through a path no one has fully mapped. And your organization’s Amazon Bedrock investment might be underused while individual teams expense separate AI tools, pulling workloads and cloud spend away from the platforms you’ve already committed to.\n\nInstead of being a people problem, this might be an architecture problem. And it surfaces the same three constraints in nearly every enterprise:\n\n**Operational fragmentation.** Each team, or sometimes even an individual developer, picks their own development toolset, including AI tooling and model selection. That fragmentation makes end-to-end governance within the software development lifecycle nearly impossible.\n\n**Security and sovereignty.** Where does prompt and code data actually flow? Who owns the logs?\n\n**Cloud spend optimization.** Commitments to key cloud providers like AWS are diluted as workloads and AI usage drift to point tools outside of customers’ existing agreements.\n\nGitLab Duo Agent Platform and Amazon Bedrock help solve this together. The division of labor is straightforward: Duo Agent Platform owns the workflow orchestration with agentic AI for software development, Bedrock owns the inference layer and hosts approved foundational models, and your organization has full control over the data and policy boundaries you already defined in AWS. Three jobs, three owners, no fragmentation.\n\n## GitLab Duo Agent Platform: The agentic control plane\n\nGitLab Duo Agent Platform is GitLab's agentic AI layer: a framework of specialized agents and flows that operate simultaneously and in-parallel, going beyond the traditional stage-based handoffs  and helping automate work across the entire software lifecycle. Rather than a single assistant responding to prompts, Duo Agent Platform enables teams to orchestrate many AI agents asynchronously using unified data and project context, including issues, merge requests, pipelines, and security findings. Linear workflows are turned into coordinated, continuous collaboration between software teams and their AI agents, at scale.\n\nWith that control plane in place, the natural next question is which AI foundation should power these agents. For customers who run GitLab Self-Managed on AWS and need inference traffic, prompt data, and logs to also stay within their AWS environment along with their software lifecycle data, Amazon Bedrock acting as the AI inference layer is the natural fit. \n\n## Amazon Bedrock: The trusted AI foundation\n\nAmazon Bedrock is a fully managed, serverless foundation model layer that runs entirely within your AWS environment. Customer data stays in the customer's AWS account: inputs and outputs are encrypted in transit and at rest, never shared with model providers, and never used to train base models. Bedrock carries compliance certifications across GDPR, HIPAA, and FedRAMP High, covering many regulated industry requirements out of the box. Teams can also bring fine-tuned models from elsewhere via Custom Model Import and deploy them alongside native Bedrock models through the same infrastructure, without managing separate deployment pipelines. Bedrock Guardrails adds configurable safeguards across all models for content filtering, hallucination detection, and sensitive data protection.\n\nTogether, GitLab Duo Agent Platform and Bedrock consolidate DevSecOps orchestration and AI model governance, helping eliminate the fragmentation that happens when teams roll out AI tools independently.\n\n## Choosing your deployment path\n\nThe integration delivers the same core GitLab Duo Agent Platform capabilities regardless of how it is deployed. What varies is who runs GitLab, who operates the AI Gateway, and whose Bedrock account the inference runs through. The right pattern depends on where your organization already operates.\n\nAt a high level, the integration has three main components:\n\n* **GitLab Duo Agent Platform:** agentic workflows embedded across the software development lifecycle  \n* **AI Gateway (GitLab-managed or self-hosted):** the abstraction layer between Duo Agent Platform and the foundational model backend   \n* **Amazon Bedrock:** the AI model and inference substrate\n\n![Deployment of GitLab and AWS Bedrock](https://res.cloudinary.com/about-gitlab-com/image/upload/v1776362365/udmvmv2efpmwtkxgydch.png)\n\nChoosing a deployment pattern is informed by where an organization wants to place the levers of control. The patterns below are designed to meet teams where they already are, whether that's SaaS-first, self-managed for compliance, or all-in on AWS with existing Bedrock investments.\n\n| Deployment Model | GitLab.com instance with GitLab-hosted AI Gateway with GitLab-operated Bedrock models   | GitLab Self-Managed with GitLab-hosted AI Gateway with GitLab-operated Bedrock models | GitLab Self-Managed  with self-hosted AI Gateway and customer-operated Bedrock models |\n| :---- | :---- | :---- | :---- |\n| **Ideal if you:** | Are primarily on GitLab.com and don’t want to self-host AI gateway and Bedrock models  | Need GitLab Self-Managed for compliance and operational reasons but don’t want to manage AI layer | Are AWS-centric with existing Bedrock usage and strict data/control needs  |\n| **Key Benefits** | Fastest, turnkey way to get Duo Agent Platform workflows: GitLab runs GitLab.com, the AI Gateway, integrated with Bedrock AI models. | Keep GitLab deployed in your own environment while consuming Bedrock models via a GitLab-managed AI Gateway, combining deployment control with simplified AI operations. | Run GitLab and AI Gateway in your AWS account, reuse existing IAM/VPC/regions, keep logs and data in your environment, and draw Bedrock usage from your existing AWS spend commitments. |\n\n## How customers use GitLab Duo Agent Platform with Amazon Bedrock\n\nPlatform teams can use GitLab Duo Agent Platform with Amazon Bedrock to standardize which models handle code suggestions, security analysis, and pipeline remediation. This helps enforce guardrails and logging centrally rather than letting individual teams adopt separate tools independently.\n\nSecurity workflows see particular benefit. GitLab Duo Agent Platform agents can propose and validate fixes for security findings within GitLab, helping reduce the manual triage work developers would otherwise handle outside the platform.\n\nFor enterprises already committed to AWS, routing AI workloads through Bedrock from within GitLab enables you to keep developer AI usage aligned with existing cloud agreements rather than generating separate, unplanned spend.\n\n## Closing the loop\n\nThe constraints that slow enterprise AI adoption are often not technical. They are organizational: fragmented tooling, ungoverned data flows, and cloud spend that never consolidates. Those are the problems that can stall AI programs even after the pilots succeed.\n\nGitLab Duo Agent Platform and Amazon Bedrock help address each one directly. Platform teams get consistent governance, auditability, and standardized paths for AI usage across the software development lifecycle. Development teams get streamlined, agentic workflows that feel native to GitLab. And AWS-centric organizations get to extend their existing Bedrock investment rather than build parallel AI infrastructure alongside it.\n\nThe result is an AI program that scales without fragmenting. Governance and velocity on the same stack, serving the same teams, under policies the organization already owns.\n\n\n> To explore which deployment pattern is right for your organization and how to align GitLab Duo Agent Platform and Amazon Bedrock with your existing AWS strategy, [contact the GitLab sales team](https://about.gitlab.com/sales/) and we’ll help you design and implement the best architecture for your environment. 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