Introducing TeamCity 2026.1: AI, Pipelines, and Enterprise CI/CD
In a Nutshell
TeamCity 2026.1 delivers AI-native CI/CD with built-in MCP server, CLI tool, and agent skills that let AI tools like Claude diagnose and fix builds through direct server access. The release advances pipelines from early access toward GA with YAML/Kotlin support, drag-and-drop UI, and bidirectional dependencies with existing build configurations. Key enterprise improvements include bundled SAML, Java 21 requirement, performance monitoring enabled by default, and GitLab webhooks for faster build triggering.
These notes were generated by AI and may contain inaccuracies.
The session is presented live from the JetBrains Munich office. Daniel serves as the solutions engineering lead on the TeamCity team at JetBrains, regularly working with both new and existing customers to advise on TeamCity incorporation into organizations. Ernst is based out of the Munich office on the product team, working on various features to improve TeamCity.
The presentation covers the overall direction of TeamCity, a recap of what's new in 2026.1, demonstrations of new capabilities including the TeamCity CLI, working between TeamCity and AI agents, and the latest advances in pipelines capabilities. Additional topics include overlooked features of TeamCity, the roadmap for the rest of 2026, additional services offered, and a questions segment.
The long-term vision focuses on building TeamCity to work with users and their AI agents. AI is bringing new challenges, greatly improving the load on CI systems, and massively changing how teams work. AI enables many new capabilities, and TeamCity is designed to work with users to help them accomplish tasks. As development moves at AI speed, TeamCity serves as a deterministic gate that helps control the non-deterministic behavior of AI on its way to production.
To balance AI integration with reliability, several focus areas are being developed. Usability improvements target both human users and AI agents, with interfaces added to ensure agent interaction remains under user control. Pragmatic AI features are being introduced, such as the analyze button that analyzes builds for users. Scalability is a priority as AI speed leads to increased build volumes in organizations. Enterprise customer needs are being addressed through direct engagement, with Daniel frequently consulting customers to understand requirements.
Sign in to read the full notes
Get access to AI-generated notes, topic timestamps, and more.