gitlabAug 13, 2026

How I built a demo generator with GitLab Duo Agent Platform (opens in new tab)

Click-through demos communicate product value more effectively than documentation, videos, or live environments, but traditionally required days of manual work. The author built a GitLab Duo Agent Platform workflow that turns screenshots and optional context into a narrated, branded HTML demo, opens a merge request, and publishes the result to GitLab Pages. The approach makes demos repeatable, reviewable, versioned, and easier to update when the product changes. ## Why Click-Through Demos Work - Users understand features faster by seeing them in action. - Click-through demos let viewers control the pace without requiring: - Access to a live product environment - Expertise navigating the product - External video-editing tools - Demos are reusable links that can be shared consistently through channels such as the GitLab Demo Hub. ## From Virtual Machines to Agents The author describes three stages in demo creation: - **Virtual machines:** Product and demo content were packaged into VMs, later hosted on centralized ESX servers. - **External SaaS tools:** Demo platforms added analytics and convenience but remained manual and dependent on vendor infrastructure. - **Agentic workflows:** Agents generate demos from prompts and deploy them within the same project, removing vendor dependency. Each stage improved accessibility, but agents make demos more sustainable by reducing repetitive work and simplifying updates. ## The Demo Creation Workflow A complete demo typically requires: - Learning the feature through documentation, issues, Slack, and discussions with product and engineering teams. - Developing a customer-focused story and scenario. - Creating a project and realistic demo data. - Capturing screenshots or video clips. - Writing step-by-step narration. - Assembling the demo in a clickable format. - Gathering stakeholder feedback and incorporating revisions. - Publishing to GitLab Docs, the Demo Hub, or other destinations. - Announcing the demo internally and externally. These activities can take days, and product changes may force the process to restart. The agent automates much of the predictable work while leaving feature selection, quality review, and final approval to humans. ## How the Click-Through Demo Generator Works - Screenshots are placed in a project folder. - An optional `context.md` file provides feature background. - An issue comment triggers the workflow. - The agent: - Renames and reorders screenshots - Writes the narration - Applies a branded HTML template - Creates a merge request - After review and merging, the demo is automatically published to GitLab Pages. - Follow-up changes can be requested from the agent. A Custom Flow invokes a custom tool that assembles the HTML, places narration panels over screenshots, and posts the merge request link back to the issue. ## Outcome-Focused Narration A project skill guides the agent’s writing so the demo explains customer value rather than merely walking through the interface. The required structure has three acts: - Open with the customer’s problem. - Show the product solving that problem step by step. - Close by explaining the value the customer gains. Localization can be added by creating a language-specific skill, allowing future demos to be translated automatically. ## Automated Publishing Publishing is also handled through an agent skill: - A demo is tagged as ready for production in the development gallery. - The agent enters the Demo Hub project. - It writes a short description, adds the current date, applies a “new” tag, and opens a merge request for the demo card. - A human still reviews and merges the request before the demo goes live. ## Demos as Code Because a click-through demo consists primarily of HTML, screenshots, and configuration, it can live in a GitLab repository rather than an isolated external tool. This enables teams to: - Review demos through merge requests. - Improve them collaboratively. - Track changes and versions over time. - Keep demos alongside the workflows that generate and publish them. The practical recommendation is to identify repetitive, well-defined work in your own organization and use GitLab Duo Agent Platform to automate its groundwork, while retaining human control over strategy, review, and release.

gitlab-duo-agent-platformhtmlagentic-workflowsmerge-requests+2

gitlabJul 27, 2026

Claude Opus 5 on GitLab: Reasoning built for the hard tasks (opens in new tab)

Claude Opus 5 is now available through GitLab Duo Agent Platform for complex, high-stakes engineering tasks. GitLab reports a 93.3% benchmark resolution rate, significantly above Opus 4.8’s 73.0%, while maintaining comparable or better speed. The model is intended for difficult debugging, large refactors, coordinated multi-agent work, and code reviews where correctness matters more than minimizing initial cost. ## Reliable Reasoning for Complex Work - Opus 5 is designed to maintain consistent reasoning across long-running tasks and multiple steps. - It aims to reduce partial patches, failed runs, diagnostic work, and repeated prompting. - In GitLab’s internal evaluation: - Opus 5 completed 100% of the tasks it attempted. - Its verified resolution rate was 93.3%, compared with 73.0% for Opus 4.8. - In one five-file SSO authentication task, Opus 5 implemented the feature, added exported types and configuration fields, committed the changes, and opened a merge request. - The model is also presented as effective for code review, identifying genuine bugs while producing relatively few false positives. ## Coordinating Multiple Agents - Opus 5 can support workflows where several agents work in parallel. - Subagent coordination helps prevent agents from interfering with one another’s changes. - Writer-verifier patterns allow one agent to check another’s output before it is accepted. - GitLab Credits usage caps can limit spending when running multiple parallel agents. ## Speed Alongside Depth - On GitLab’s most difficult benchmark tasks, Opus 5 was faster at the 95th percentile: - 768 seconds for Opus 5 versus 784.98 seconds for Opus 4.8. - 768 seconds versus 982.57 seconds for Sonnet 4.6. - GitLab argues that this combination of speed and reliability produces more predictable completion times for long tasks. ## Choosing the Appropriate Model - Sonnet-class models remain suited to routine development because they are fast, affordable, and dependable. - Opus 5 is recommended for: - Difficult debugging - Large refactors - Complex multi-file features - Decisions where rework would be costly - Model selection is configurable within GitLab, while all models use the same context management, policy controls, and audit trail. ## Availability - Claude Opus 5 is available on GitLab Duo Agent Platform and uses GitLab Credits. - New users can start with a free trial. - GitLab Premium and Ultimate subscribers can enable Duo Agent Platform and use included credits. Opus 5 is best viewed as a specialized model for high-complexity work rather than a universal replacement for faster Sonnet-class models. Teams should match the model to task difficulty and use spending limits when scaling autonomous, parallel-agent workflows.

gitlab-duo-agent-platformai-agentcode-reviewrefactoring+4

gitlabJul 20, 2026

Automate work item assignment with GitLab Duo (opens in new tab)

The GitLab Duo Agent Platform’s new **“Work item created”** trigger automates triage as soon as an issue or other work item is created. A custom flow can assess team workloads and assign the item without manual intervention, improving speed and workload balance. The post demonstrates this using two agents and GitLab Orbit. ## The Problem with Manual Assignment - Team members must repeatedly check capacity, workload, meetings, and PTO before routing each work item. - Manual triage becomes slow and inconsistent as issue volume grows. - Previously, GitLab Duo Flows required a human action—such as a mention or assignment—to start. ## How the Trigger Works - The trigger fires immediately when a work item is created in a configured project. - Flows run continuously in the background without requiring someone to initiate them. - This enables instant, scalable routing while developers focus on higher-value decisions. ## Benefits of Automated Triage - Assigns and routes work immediately. - Handles anything from a single issue to hundreds of items. - Applies workload and availability criteria consistently. - Removes repetitive assignment work from team leads and developers. ## Example: The “Work Item Assigner” Flow - The demonstrated flow runs in the `Intra-account-transfers` project. - Its first agent uses GitLab Orbit to determine each resource’s number of open work items across the top-level group. - A second agent identifies the least-loaded person and assigns the new work item to them. - When a new issue is created, the flow activity log shows both agents executing their steps. - In the example, the issue is automatically assigned to William, who has the lightest workload. ## Potential Enhancements - Connect the flow to HR or PTO systems through Model Context Protocol (MCP) to account for upcoming leave. - Integrate team calendars to consider real-time availability when making assignments. The post recommends using the “Work item created” trigger with workload-aware agents to automate routine assignment decisions, achieve faster triage, and distribute work more evenly.

gitlab-duo-agent-platformai-agentmodel-context-protocolworkflow-automation+3

gitlabJul 16, 2026

Forrester Consulting: GitLab Duo Agent Platform delivers 400% ROI (opens in new tab)

GitLab-commissioned Forrester research found that organizations using GitLab Duo Agent Platform could achieve a 400% three-year ROI, $7.5 million in net present value, and payback in under six months. The study argues that agentic coding creates the greatest business value when integrated across the software lifecycle—not merely used to generate code. Benefits included faster onboarding, shorter migrations, quicker security remediation, and more developer time for feature work. ## Study Scope and Financial Model - Forrester interviewed four organizations across financial services, software, entertainment, and insurance. - Their experiences were modeled as a composite global company with: - $3 billion in annual revenue - 3,000 employees - GitLab Duo Agent Platform adoption growing from 150 to 250 users - Three-year risk-adjusted costs totaled approximately $1.9 million: - $1.3 million in consumption credits - $589,000 for implementation, training, support, and internal labor - Quantified benefits reached $9.4 million, producing: - 400% ROI - $7.5 million net present value - Payback in less than six months ## Problems Before Adoption - Manual processes and ad-hoc knowledge sharing slowed development. - New developers depended heavily on senior engineers for context and troubleshooting. - Security fixes waited in queues for specialists with the necessary expertise. - Code review was often a larger bottleneck than writing code. - Senior engineers were repeatedly interrupted to unblock other team members. ## Quantified Benefits - **80% faster developer onboarding** - Agentic chat in IDEs and repositories helped new hires understand unfamiliar codebases independently. - Estimated savings: $582,000. - **75% shorter migration timeline** - A migration from on-premises GitLab to GitLab SaaS finished in two months instead of eight. - Agents helped diagnose pipeline failures and resolve issues during the migration. - Estimated labor savings: $157,000. - **40% more time for security and QA engineers** - Contextual explanations and suggested fixes reduced remediation effort and reliance on senior staff. - Estimated three-year savings: $1.3 million. - **20% more developer capacity for feature work** - Agents supported code review, testing, and troubleshooting. - Estimated combined benefit: $7.4 million. - Additional unquantified benefits included reduced need for overlapping AI tools, improved developer satisfaction, and stronger knowledge sharing. ## Broader Impact on Software Delivery The study found that organizations shipped features in days rather than weeks, resolved vulnerabilities more quickly, onboarded staff faster, and compressed major infrastructure work. Its central conclusion is that AI productivity gains compound when agentic coding is connected to infrastructure supporting the entire software delivery lifecycle. The findings are based on interviewed organizations and a composite financial model, so actual results will vary. Companies evaluating agentic development should use the study as a framework for estimating benefits across productivity, security, onboarding, and operational efficiency—not as a guaranteed ROI.

gitlab-duo-agent-platformai-agentdevsecopscode-review+3

gitlabJul 16, 2026

Turn multi-step software delivery into agentic flows you can trust (opens in new tab)

GitLab Duo Agent Platform’s Custom Flows are now generally available, enabling teams to automate multi-step software delivery rather than relying on chat responses, scripts, or manual runbooks. Flows can start from GitLab events or Agentic Chat, execute tasks across projects, and include human approval checkpoints. The goal is dependable, repeatable automation for work such as implementing issues, repairing pipelines, and reviewing merge requests. ## Why Software Delivery Remains Manual - Real development tasks involve multiple steps: gathering context, changing code, opening a merge request, waiting for CI, and responding to reviews. - Without workflows, people must manually coordinate each handoff and remember undocumented team practices. - Teams previously hesitated to formalize these processes because Custom Flows lacked production readiness and sufficient event coverage. ## What Agentic Flows Provide - Automate trusted sequences triggered by events such as: - Mentions and assignments - Pipeline activity - Merge request lifecycle changes - Work item updates - Run under a composite identity, keeping permissions bounded and actions attributable. - Launch specialist workflows from Agentic Chat, including: - Developer Flow for implementing changes - Code Review Flow for reviewing merge requests - Fix CI/CD Pipeline Flow for diagnosing and repairing failures - Require user approval before handing work from chat to a flow, while displaying progress inline. - Use review exclusions to avoid automatically reviewing bot-authored or branch-pattern merge requests. - Apply custom review instructions so automated reviews match team standards. ## Custom Flow Configuration - Create flows from a project or the AI Catalog. - Set visibility, enable flows for selected projects, and attach GitLab event triggers. - Add human-in-the-loop checkpoints for sensitive operations. - GitLab 19.2 adds: - A “work item status changed” trigger - Bulk enablement of public flows across up to 100 projects - A future Flow Creation Agent is planned to generate runnable flow definitions from plain-language descriptions. ## Costs and Adoption - Event-driven flows consume GitLab credits according to the work they perform. - GitLab recommends testing flows in a small number of projects before broad deployment. - Access is available through a GitLab Duo Agent Platform trial or through included credits for Premium and Ultimate subscribers. Teams should begin by encoding a few well-understood delivery processes as Custom Flows, adding approval gates where necessary, and expanding gradually while monitoring credit usage and automation quality.

gitlab-duo-agent-platformai-agentci-cdagentic-workflows+3

gitlabJul 8, 2026

How we used AI agents to migrate GitLab rate limiting (opens in new tab)

GitLab used a three-person engineering pod and AI agents to migrate 121 application-level rate-limit keys into a shared `labkit-ruby` implementation. The migration succeeded because humans retained ownership of architecture, scope, rollout decisions, and final review while agents handled mechanical coding, tests, and reviews. The main lesson was that disciplined workflows and meaningful observability mattered more than the agents themselves. ## Migration Setup - GitLab was consolidating two production rate-limiting systems: - `Gitlab::ApplicationRateLimiter`, with 121 keys - A separate Rack-level implementation - The target was a single observable, testable, and consistently operated implementation in `labkit-ruby`. - A three-person pod divided responsibilities across the monolith, the gem, architecture, and project scope. - AI agents: - Read project context - Drafted specifications - Implemented bounded changes - Wrote tests - Pre-reviewed merge requests - Humans controlled scope, architecture, rollout strategy, and final approvals. ## The Specification and Review Loop - The team followed a repeatable process: - Read the epic - Write a specification - Conduct adversarial review - Implement only after blockers were resolved - Verify with explicit evidence - Review the merge request adversarially - Escalate to human review - Merge - Adversarial review was limited to two resolution rounds before requiring human involvement. - The project produced 14 numbered specifications and more than 30 merge requests. - This structured loop made agents useful on legacy code without allowing them to make high-impact decisions independently. ## Successful Rollouts - The first cohort covered five heavily used keys, including: - `pipelines_create` - `notes_create` - `user_sign_in` - Rollout progressed from 1% to 10%, 50%, and finally 100% over two days. - Engineers compared the old and new implementations during rollout and deliberately generated traffic to test behavior above the configured limits. - The second cohort consolidated 95 call sites: - 83 in the monolith - 12 in Enterprise Edition - Agents were especially effective at this repetitive, large-scale codebase work, avoiding roughly 95 individual feature-flag changes and 190 YAML edits. ## Observability and Shadow-Mode Failure - During Cohort 2, an adapter dropped an identifier on an unauthenticated path by incorrectly packing three strings into two primitive slots. - Some users briefly received generic failures when enforcement began. - Shadow comparison had detected divergence, but the dashboards did not distinguish structural identifier collisions from ordinary disagreements. - The team disabled enforcement immediately and shipped a short-term fix two days later. - The deeper cleanup will replace array-based scopes with named characteristics when calling `ApplicationLimiter`. - The incident showed that having observability is insufficient if it cannot identify the failure modes that require action. ## Missed Rate Limits and Infrastructure Constraints - An audit revealed that the original five-cohort plan had missed 17 of the 121 keys. - The omissions included: - Enterprise-only limits - Registry entries - Webhook keys - `partner_*` sub-second limits - Orphaned adapter rows - The team had not maintained a complete inventory count, making it possible for keys to become effectively invisible. - A sixth cohort was added to cover the missed cases. - Redis capacity also became a constraint: - The rate-limiting service used a four-shard cluster. - `maxclients` was increased incrementally. - Rollout stopped at 75,000 connections rather than 100,000 because primary CPU usage approached saturation. - Redis command execution was limited by one core per primary, leaving no simple vertical scaling solution. ## How AI Changed the Work - Agents made code generation faster, shifting the bottleneck to: - Human review capacity - Rollout judgment - Operational monitoring - Reviewer and operator attention - Agent collaboration was not always efficient; engineers sometimes spent longer guiding agents than they would have spent coding directly. - Engineers also had to develop new skills for specifying, reviewing, and correcting agent-generated work. - Agents could execute a request mechanically—such as creating dozens of feature flags—but could not decide whether that design was appropriate. - Human judgment remained essential for simplifying the rollout and avoiding unnecessary per-key flags. ## Outcome - By mid-June, all six cohorts had reached 100%. - All 121 application rate-limit keys were running through the new framework. - The migration demonstrated that AI agents can safely support complex legacy-system changes when paired with bounded tasks, adversarial review, gradual rollouts, complete inventories, and failure-specific observability. A practical recommendation is to use agents for repetitive implementation and verification, but keep architecture, risk assessment, rollout control, and operational decisions firmly with experienced humans.

gitlab-duo-agent-platformai-agentobservabilityredis+4

gitlabJun 30, 2026

Claude Sonnet 5 on GitLab: More reliable, more efficient (opens in new tab)

Claude Sonnet 5 is now available on GitLab Duo Agent Platform through GitLab’s AI Gateway across all tiers and deployment models. GitLab reports that it is the first model to complete every task in its evaluation suite, compared with 93.8% for Sonnet 4.6, while resolving 8.8% more issues. The result is intended to make software-engineering agents more reliable, efficient, and suitable for production-scale workflows. ## More Reliable Agent Runs - Sonnet 5 is designed for multi-step development tasks, code generation, and automated workflows. - Completing every benchmark task reduces failures that require diagnosis, reprompting, and verification. - GitLab highlights applications including: - Multi-file refactoring - Test generation - Security investigations across repository history - Pipeline-failure analysis - Higher completion rates allow developers to review agent output instead of repeatedly restarting incomplete runs. ## Lower Cost Through Efficiency - Reliability and resource efficiency reduce the effective cost of completed agent tasks. - Models on GitLab Duo consume GitLab Credits at different rates. - Teams can control spending by using a model whose cost and performance fit routine development work. - GitLab directs users to its Credits documentation for model-specific consumption rates. ## Selecting the Right Model - Sonnet 5 is positioned as a dependable default for everyday software-development tasks. - Sonnet-class models aim to balance quality, speed, and cost. - Claude Opus 4.8 remains available for complex, long-running tasks requiring greater reasoning depth. - Teams can select models per task through model selection in their GitLab instance. ## Availability - Claude Sonnet 5 is available immediately on GitLab Duo Agent Platform through the AI Gateway. - It runs on GitLab Credits and is offered across all tiers and deployment models. - New users can start through a Duo Agent Platform trial or GitLab Free, while Premium and Ultimate subscribers can use included credits. Overall, GitLab presents Claude Sonnet 5 as a more reliable and economical choice for routine agent-assisted development, with more powerful models available when tasks require deeper reasoning.

gitlab-duo-agent-platformai-agentdevsecopsai-gateway+2

gitlabJun 25, 2026

Google Antigravity agents get full context with GitLab Orbit (opens in new tab)

Google Antigravity agents can now access GitLab’s broader software lifecycle context through GitLab Orbit, available in the Antigravity MCP Store. Orbit builds a knowledge graph connecting code, projects, dependencies, merge requests, pipelines, users, and vulnerabilities, allowing agents to answer questions that would otherwise require manual research across GitLab. GitLab reports that internally tested agents using Orbit were up to 11 times faster, used 4.5 times fewer tokens, and hallucinated up to 45 times less. ## GitLab Orbit Adds Lifecycle Context - Antigravity agents can normally access files and terminals but lack information about: - Service dependencies - Related vulnerabilities - Merge request history - Code ownership and reviewers - Pipeline performance - Orbit indexes GitLab and exposes its knowledge graph through: - `query_graph` for structured lifecycle queries - `get_graph_schema` for available node types, properties, and relationships - Agents receive typed results through GitLab Orbit’s JSON query DSL. ## Practical Development Use Cases ### Blast radius analysis - Engineers can identify which projects depend on a module before refactoring it. - Agents can also find open merge requests affecting those files and identify their owners. - This reveals conflicts and stakeholders before changes are made. ### Onboarding and codebase exploration - Agents can explain a service’s dependencies, entry points, and recently opened merge requests. - Results can be captured in a persistent Walkthrough Artifact rather than disappearing in chat. - Orbit reindexes changes within minutes, keeping documentation more current than static wikis. ### Live architecture diagrams - Agents can query service dependencies and render them as diagrams using Nano Banana Pro. - Diagrams can be filtered, such as showing only services with unresolved security findings. - Access controls apply to every query, making generated diagrams safe to share. - GitLab is developing similar functionality through its Software Architecture Map. ## Installation Through the MCP Store - Open Antigravity settings and go to the customization tab. - Find the MCP section and select **Add MCP**. - Choose GitLab Orbit and authenticate with GitLab. - No configuration files or terminal commands are required. ## Availability and Technical Details - Orbit uses the same context engine as GitLab Duo Agent Platform. - It supports code indexing for Ruby, Java, Kotlin, Python, TypeScript, JavaScript, Rust, and C#. - Code is indexed from the default branch and refreshed within minutes. - MCP queries consume GitLab Credits, while `get_graph_schema` calls are free. - The feature is available to GitLab Premium and Ultimate customers on GitLab.com. For teams already using GitLab and Google Antigravity, GitLab Orbit provides a governed, current context layer that can make agents more accurate and useful. Teams should enable Orbit for a top-level group and install it through the Antigravity MCP Store, while accounting for GitLab tier requirements and credit usage.

gitlab-duo-agent-platformmodel-context-protocoldevsecopsgitlab-orbit+3

gitlabJun 17, 2026

GitLab and Capgemini accelerate DevSecOps transformation (opens in new tab)

GitLab and Capgemini have formed a global alliance to help organizations modernize software delivery and adopt secure, AI-assisted development practices. Capgemini will provide GitLab’s DevSecOps platform, including GitLab Duo Agent Platform, alongside implementation and transformation services. The partnership aims to shorten the journey from platform adoption to measurable business results. ## Partnership and Client Benefits - Capgemini becomes a GitLab Select Partner serving clients globally. - Customers receive expert guidance on tools, processes, and transformation methodologies. - GitLab Duo Agent Platform will help orchestrate AI across the software development lifecycle. - The combined offering is designed to accelerate delivery while improving software supply-chain security. ## Initial Areas of Focus - **Cloud-native development and application modernization:** Moving legacy workloads to modern architectures. - **Sovereign solution design and delivery:** Addressing regulatory, regional, and data-residency requirements. - **Value stream modernization:** Improving the path from initial idea through production release. - **Generative and agentic AI:** Integrating GitLab Duo Agent Platform into development workflows to help teams ship faster. Organizations interested in the alliance’s services can contact GitLab or Capgemini representatives.

gitlab-duo-agent-platformgen-aigitlabdevsecops+3

gitlabJun 10, 2026

GitLab Flex: Commit once, reshape your seats and AI spend (opens in new tab)

GitLab Flex addresses the unpredictability introduced by agentic software development, where seat counts, AI consumption, and desired capabilities can change throughout the year. Instead of fixing these needs in a traditional annual contract, Flex provides one annual spending commitment that can be reallocated monthly. GitLab’s conclusion is that organizations can adopt new capabilities and adjust usage without renegotiation or re-procurement. ## Fixed Contracts, Moving Needs - Agentic development creates uncertainty around: - The number of platform seats required as teams and contractor mixes change. - The amount of AI usage driven by evolving use cases and technology. - Which new capabilities organizations will adopt during the contract term. - Traditional contracts require customers to estimate all three in advance. - Overestimating leads to unused seats and capacity, while underestimating can delay adoption through additional procurement cycles. ## One Annual Commitment, Adjusted Monthly - GitLab Flex uses a single annual dollar commitment based on a published rate card. - Customers can allocate that commitment across: - Premium and Ultimate platform seats. - GitLab Credits for services such as Duo Agent Platform, hosted runners, and artifact management. - Eligible usage-based capabilities introduced after signing. - It applies across GitLab.com, Self-Managed, air-gapped, and Dedicated deployments. - Customers can shift unused seat reservations toward other seats or AI usage without amending the agreement. - Usage above the annual commitment is billed on demand at $1 per credit or the negotiated per-seat rate. ## Combined Seats, Credits, and Deployment Types - A single agreement can combine platform seats and credit-metered services. - Larger commitments provide volume discounts across the rate card. - Organizations can change their mix of seats, credits, and deployment models during the term. - Unlike models that separate licenses and usage credits, Flex allows budget to move between them. ## Pricing and Spending Controls - Reserved capacity is priced below unplanned usage. - Subscription-level and per-user caps help control spending. - Project- and group-level administrative controls provide additional oversight. - Unreserved seats use the same effective negotiated rate as reserved seats. - Cloud-connected customers are billed automatically; air-gapped customers are invoiced twice yearly. ## Existing Contracts and Availability - GitLab Premium and Ultimate remain available through direct seat pricing. - Existing customers may keep their current plans through renewal. - Flex does not change the capabilities included in those tiers. - Customers approaching renewal can compare Flex with their current contract using projected seat and AI usage. - Flex orders are available now, with fulfillment rolling out throughout the quarter. GitLab Flex is best suited to organizations that expect their workforce, AI consumption, or deployment requirements to change frequently. Its main benefit is financial and operational flexibility: one agreement lets customers rebalance spending monthly instead of waiting for renewal or reopening procurement.

gitlab-duo-agent-platformaigitlabdevsecops+4

gitlabJun 10, 2026

GitLab on Google Cloud: Fully managed, compliant, and AI-ready (opens in new tab)

GitLab is introducing a fully managed deployment on Google Cloud through certified managed service providers such as Beyond and Digital Future. The offering combines data residency and compliance controls with access to Google’s Gemini and Gemma models through GitLab Duo Agent Platform. Organizations can also purchase the platform through Google Cloud Marketplace, applying existing cloud commitments to GitLab, AI inference, and infrastructure costs. ## Fully Managed GitLab on Google Cloud - Certified MSPs operate GitLab on Google Cloud under service-level agreements, removing infrastructure-management responsibilities from customer teams. - Organizations retain control over where code, pipelines, and security data are stored, supporting sovereignty and data-residency requirements. - GitLab’s audit and policy controls provide visibility into agent actions, merge requests, and security findings. ## AI Model Choice for Different Workloads - Gemini models, including Gemini 3.5 Flash, are available in Duo Agent Platform through Google’s Gemini Enterprise Agent Platform. - GitLab’s participation in Google’s early-access program is intended to bring new Gemini models to Duo as they become available. - Regulated or self-hosted teams can use Gemma 4 with GitLab Duo Self-Hosted. - With self-hosted models, the AI Gateway and all requests and responses remain within an organization’s on-premises or private-cloud environment. ## Using Existing Google Cloud Commitments - GitLab and Duo Agent Platform can be purchased through Google Cloud Marketplace. - Existing Google Cloud commitments can fund GitLab subscriptions, model inference, and related infrastructure without starting a new procurement cycle. - Consolidated Google Cloud billing reduces reconciliation across vendors. - GitLab retains its own cost-management features, including usage dashboards, model policies, and GitLab Credits for more predictable AI spending. ## One Governed DevSecOps Platform - GitLab Duo Agent Platform provides software-delivery context that standalone coding assistants lack, including merge requests, pipelines, and deployment targets. - This context helps agents perform multi-step work and supports code review at monorepo scale. - Combining GitLab’s governance and lifecycle data with Google’s models keeps deployment, model selection, compliance, and spending aligned in one platform rather than fragmented across multiple tools. Organizations can start with a Duo Agent Platform trial, enable it through the free GitLab tier, or use included GitLab Credits with Premium and Ultimate subscriptions. Overall, the offering is aimed at teams that want managed GitLab operations, flexible AI model access, and strong control over data location and costs on Google Cloud.

gitlab-duo-agent-platformgitlabdevsecopsgemini+4

gitlabJun 9, 2026

Mythos-class Claude Fable 5 arrives on GitLab Duo Agent Platform (opens in new tab)

Claude Fable 5, Anthropic’s Mythos-class model, is now available through GitLab Duo Agent Platform across all tiers and deployment models. The post presents it as a major step beyond incremental model improvements, emphasizing stronger first-attempt accuracy, sustained autonomous workflows, and better code review and incident analysis. Its main conclusion is that teams should apply it to complex, previously difficult engineering problems rather than routine tasks. ## More Accurate First-Pass Results - Produces more correct implementations for complex, well-defined problems with fewer iterations. - Reduces back-and-forth in Duo Agentic Chat. - Performs especially well on: - Multi-file refactoring - Incident investigation - Infrastructure-as-code definitions - Interprets technical images, web applications, and detailed screenshots more accurately while often using fewer output tokens. ## Longer and More Reliable Agent Workflows - Sustains goal-directed work across extended, multi-day runs and millions of tokens. - Maintains instructions and task focus without frequent manual checkpoints or re-prompting. - Uses verification loops to identify and correct its own mistakes. - Handles parallel sub-agents more reliably across multiple repositories or services. - Reduces the amount of human oversight required per agent run, allowing teams to review results asynchronously. ## Improved Bug Detection and Incident Response - Provides higher bug-finding recall than previous models. - Improves outage triage, root-cause analysis, and repository-history investigation. - Finds deeper code-path issues and edge cases during merge request reviews. - Produces more actionable review comments instead of generic feedback. - Helps reduce production defects and improve mean time to resolution. ## Recommended Use Cases - The post recommends testing the model on difficult, unsolved engineering problems rather than routine work. - Suggested applications include: - Complex multi-file refactors - Production incident investigations - Implementations developers would normally write manually - Teams should allow agents to scope work, ask clarifying questions, and execute multi-step solutions. Claude Fable 5 became available on GitLab Duo Agent Platform on June 9, 2026. Teams can access it through a free trial, the GitLab free tier, or included GitLab Credits for Premium and Ultimate subscribers.

gitlab-duo-agent-platformai-agentci-cddevsecops+4

gitlabMay 21, 2026

Transform MRs from manual tasks to an automated workflow (opens in new tab)

GitLab 19.0 expands Developer Flow from generating merge requests to managing much of their entire lifecycle. Its AI agent can respond to reviews, investigate codebases, resolve conflicts, and split oversized MRs, while automation handles rebasing and merging. The result is less manual effort between opening and merging an MR, with developers supervising rather than executing every step. ## Developer Flow Across the MR Lifecycle - Can be triggered from: - An issue via **Generate MR** - An issue or MR assigned to the **Duo Developer** service account - Any issue or MR discussion using the new **@mention** trigger - Continues working on the same MR instead of creating separate changes to reconcile. - Handles: - Multiple rounds of reviewer feedback - Merge conflicts on long-running branches - Codebase research and technical evaluations - Oversized MR splitting - New feature implementation - Uses a single agentic loop with tools such as `read`, `grep`, file editing, and command execution. - Reads `AGENTS.md` for project conventions and operational guidance. - Uses `agent-config.yml` to configure dependencies, tooling, tests, and pre-commit hooks. These capabilities are available through GitLab Duo Agent Platform on Premium and Ultimate plans. ## Autonomous Merge Conflict Resolution - The beta **Resolve with Duo** button is available on the MR conflict page and merge checks widget. - The agent: - Reviews the MR’s intent and both branches - Selects a resolution strategy - Edits conflicting files - Commits and pushes the resolution - It leaves a summary comment explaining the conflict and resolution path. - If it cannot resolve the conflict safely, it reports that rather than guessing. ## One-Click Rebase and Merge - The beta feature combines rebasing and merging into one action. - It is designed for teams using semi-linear or fast-forward merge methods. - It is available on Free, Premium, and Ultimate tiers. ## Reducing Manual MR Work GitLab distinguishes between AI-driven judgment and mechanical automation: - AI handles code changes, reviewer feedback, and conflict resolution. - Automation handles tasks such as rebasing before merge. - Together, these features reduce the time developers spend on repetitive MR maintenance while preserving human oversight for steering, reviewing, and final decisions. Developers can try Developer Flow through a GitLab Duo Agent Platform trial. Existing Premium and Ultimate users with the platform can use it on merge requests, while older GitLab versions may require manually configuring the mention trigger.

gitlab-duo-agent-platformai-agentgitlabdevsecops+4

gitlabMay 21, 2026

More AI models for GitLab Duo Agent Platform Self-Hosted (opens in new tab)

GitLab 19.0 expands open source model support for Duo Agent Platform Self-Hosted, giving regulated and air-gapped teams more capable AI options without sending source code to external APIs. The update supports selecting different models for different workflows and enables both fully on-premises and hybrid deployments. GitLab’s goal is to reduce the capability gap between isolated environments and cloud-based AI services. ## Challenges for Regulated and Air-Gapped Teams - Data residency, compliance rules, and network isolation often prohibit third-party AI APIs. - Air-gapped environments must run inference locally because they have no internet or external connectivity. - Teams have traditionally faced a trade-off between using an underpowered model and deploying an unnecessarily large model for routine tasks. - These constraints have limited AI productivity gains in highly regulated environments. ## Expanded Open Source Model Support GitLab evaluated models for: - Multi-step tool use - Instruction adherence - Code generation - Reasoning across large diffs and multi-file codebases Newly supported models include: - Mistral Devstral 2 123B - GLM-5.1 - Kimi-K2.6 - MiniMax-M2.7 ## Deployment Options - The recommended setup uses on-premises hardware with vLLM for model serving. - Organizations can also deploy models on GPU-enabled virtual machines in private clouds. - Both approaches keep data within the organization’s controlled environment. - Fully air-gapped teams should use locally hosted models and consult hardware requirements for each model. - Hybrid deployments can combine self-hosted and GitLab-managed models on a per-feature basis. ## Availability and Licensing - Offline-license customers need the GitLab Duo Agent Platform Self-Hosted add-on. - Online-license customers can use usage-based models and combine self-hosted and GitLab-managed models. GitLab recommends choosing models and infrastructure based on network isolation, compliance requirements, hardware availability, and workflow needs. The expanded support makes self-hosted AI a more practical option for organizations that require strict control over their code and data.

gitlab-duo-agent-platformvllmgpu-inferenceself-hosted-ai+3

gitlabMay 7, 2026

Automate deployment processes with GitLab Duo Agent Platform (opens in new tab)

GitLab Duo Agent Platform can automate the complex, repetitive work of onboarding a microservice into an established GitOps workflow. By analyzing an application’s repositories and configuration, a custom agent can generate manifests, update pipelines, configure image automation, and follow organization-specific conventions. The approach combines AI-driven speed with GitLab-managed versioning, governance, and enterprise security. ## TanukiBank’s GitOps Use Case - The fictional TanukiBank application needs a new `intra-account-transfers` microservice for its Quick Transfer feature. - Its deployment architecture includes: - Individual service projects with container registries and build pipelines. - **Tanuki Bank - Delivery**, which stores deployment manifests and delivery pipelines. - **Flux Config**, which contains Flux manifests for Kubernetes. - Flux Image Automation watches service registries and updates corresponding delivery manifests. - A delivery pipeline then builds and signs the image, while Flux CD synchronizes it to the Kubernetes cluster. - Adding a service manually requires coordinated changes across all these components. ## Generating the Custom Agent’s System Prompt - GitLab Duo Agentic Chat examines the TanukiBank group, subgroups, source files, Dockerfiles, manifests, configuration, and dependencies. - It generates a detailed system prompt describing: - The existing GitOps workflow. - Required operating rules. - Reporting instructions. - Recommended tools. - The prompt is specific to the workflow at the time it is generated. - If the application’s GitOps process changes, the prompt should be regenerated. ## Creating and Configuring the Agent - A new `application-agents` project manages custom agents, their administrators, and where they can run. - A managed agent named **TanukiBank Microservice Onboarder** is created with: - A description. - The generated system prompt. - Tools recommended by GitLab Duo. - The agent is enabled in both **Tanuki Bank - Delivery** and **Flux Config**. - Its presence in each project’s Agentic Chat agent selector confirms that it is available. ## Creating the Microservice - A new `services/intra-account-transfers` project is created. - GitLab Duo’s **Developer** foundational flow implements the service from an issue specification. - The flow: - Reads the requirements. - Writes the implementation. - Creates a branch and merge request. - Links the merge request to the issue. - After local verification with `curl`, the merge request is merged and the project pipeline publishes container images. - At this stage, the service exists, but the GitOps system has not been updated: - `manifests/dev` has no service manifests. - The delivery pipeline does not reference the service. - `Flux Config` lacks an `image-update-automation.yaml` entry. ## Using the Custom Onboarding Agent - The custom agent is enabled in the new service project. - From **Tanuki Bank - Delivery**, the user selects **TanukiBank Microservice Onboarder** in Agentic Chat and provides the service name and hostname. - The agent begins onboarding by: - Finding and reading the service’s Dockerfile. - Determining the application port. - Generating the required Kubernetes manifests. - Updating the relevant delivery pipelines. - This automates the coordinated repository changes normally required for a new microservice. ## Practical Takeaway A custom GitLab Duo agent is most valuable when it is grounded in an organization’s real repositories and deployment conventions. Generate its prompt from the current system, keep the agent centrally governed, and regenerate the prompt whenever the GitOps workflow changes.

gitlab-duo-agent-platformk8sci-cdmicroservices+3