GitLab Inc. has announced the release of GitLab 18.11, introducing expanded agentic AI capabilities across the entire software development lifecycle. With this update, the company aims to address growing inefficiencies caused by the rapid rise of AI-generated code, particularly in areas such as security remediation, pipeline configuration, and delivery analytics.

As organizations increasingly adopt AI for faster code generation, they are encountering what GitLab describes as the “AI Paradox.” While development speeds have improved, supporting systems such as security, operations, and delivery processes have struggled to keep pace. Consequently, teams are facing growing backlogs of pipeline configurations, unresolved security findings, and unanswered delivery insights.

To tackle these challenges, GitLab 18.11 introduces platform-native AI agents that operate directly within the existing development environment. These agents have built-in access to code repositories, pipelines, issues, and security data, enabling them to streamline workflows without requiring developers to switch contexts.

One of the most significant updates is the general availability of Agentic SAST Vulnerability Resolution for GitLab Ultimate users on the GitLab Duo Agent Platform. This feature directly addresses a major pain point in modern development. According to GitLab’s 2025 DevSecOps Report, developers spend an average of 11 hours per month fixing vulnerabilities after deployment. Now, when a SAST scan is completed, the AI agent automatically identifies verified issues, generates a code fix targeting the root cause, and creates a ready-to-merge request with a confidence score. As a result, developers can resolve vulnerabilities faster and prevent them from reaching production.

In addition, GitLab has introduced two new prebuilt agents to simplify CI/CD and analytics workflows. The CI Expert Agent, currently in beta, analyzes a repository to identify its programming language and framework. It then generates a build-and-test pipeline using natural language, allowing teams to launch pipelines within minutes without manually writing YAML configurations. This significantly lowers the barrier to CI adoption.

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Meanwhile, the Data Analyst Agent, now generally available, enables users to query software lifecycle data using natural language. It provides instant visual insights into key metrics such as merge request cycle times, pipeline performance, and deployment frequency. Therefore, teams can gain actionable insights without relying on complex dashboards or query languages. Notably, this feature is accessible across Free, Premium, and Ultimate tiers.

Furthermore, GitLab 18.11 introduces enhanced usage controls to help organizations manage AI-related costs more effectively. Through subscription-level and per-user spending caps for GitLab Credits, enterprises can maintain predictable budgets while scaling AI adoption. Additionally, administrators gain full visibility into usage patterns via the GitLab Credits dashboard and customer portal.

“Much of the AI investment in software development has focused on writing code faster. The bigger opportunity is what comes next,” said Manav Khurana, chief product and marketing officer at GitLab. “Agents are only as effective as the context they can access. GitLab 18.11 extends our agents deeper into security, pipelines, and delivery analytics, where that context already lives. That’s how GitLab is defining the future of software engineering in the AI era.”

Overall, GitLab 18.11 represents a significant step forward in integrating AI across DevSecOps workflows. By embedding intelligent agents directly into development processes, GitLab is helping organizations bridge the gap between rapid code generation and efficient, secure software delivery.

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