JetBrains Air Teams: Agentic Workflows for Engineering Teams
Shared workspace automates repetitive tasks across development lifecycle
1From individual to team agents
JetBrains evolved its Air platform from individual developer tools to team-level solutions after identifying productivity bottlenecks. While solo developers gained efficiency with agents, coordination gaps emerged when workflows couldn't scale across teams. Air Teams addresses this by providing shared cloud environments, reusable automations, and centralized project management - moving agentic work from personal laptops to collaborative spaces.
2Four core components
The platform consists of: 1) Automations for recurring tasks like code reviews and dependency updates, 2) Shared cloud environments with preconfigured tools, 3) Cloud tasks that run independently of local machines, and 4) Team projects with role-based access. Each component eliminates manual overhead - environments need setup just once, automations trigger without human initiation, and tasks persist across devices.
3Self-running automations
Air Teams introduces event-driven workflows that execute without constant human oversight. Examples include automatic PR reviews upon creation, scheduled dependency updates twice weekly, and labeled-issue triage. Each automation combines instructions, environment specs, and triggers (events/schedules). Teams can modify 10 included templates or create custom workflows, with all runs logged for transparency and debugging.
4Environment standardization
Shared cloud environments ensure consistency by packaging tools, dependencies, and credentials into reproducible configurations. Teams can manually configure or have agents propose startup scripts via PRs. Environments snapshot after setup, becoming reusable assets versioned alongside code. This eliminates individual setup variance while maintaining security - secrets remain encrypted even when shared across automations.
5Cloud task flexibility
Tasks execute in dedicated cloud environments, freeing local resources and continuing work after device disconnection. Users can initiate tasks from IDEs or browsers (mobile coming soon), with progress monitoring across devices. The system intelligently manages concurrent runs - for dependency updates, it maintains one current PR, closing stale ones to prevent merge conflicts.
6Team project structure
Projects organize members, environments, and automations with two roles: Admins manage infrastructure while Members use shared resources. Credit allocation separates personal from project spending, ensuring automations persist beyond individual contributors. This model prevents knowledge silos where workflows depend on specific developers' local setups.
7Noise control mechanisms
To prevent AI-generated spam, Air Teams keeps humans in the decision loop. All code changes arrive as PRs requiring manual review. Automations can be constrained to produce minimal output (e.g., one active dependency PR). Full agent reasoning chains remain visible for auditability when results seem questionable, balancing automation with oversight.
8Real-world use cases
JetBrains runs three production automations: 1) PR reviews where agents track fix progress across commits, 2) Label-triggered bug fixes that contextually attempt repairs, and 3) Dependency management that tests upgrades and automatically reverts breaking changes. Each demonstrates how narrowly-scoped, pattern-based tasks benefit most from automation.
9Implementation strategy
The rollout prioritizes business customers first, with individual plans coming later. Adoption follows a gradual path: teams start with template automations, customize environments, then develop bespoke workflows. JetBrains emphasizes collaborative onboarding - since value comes from shared use, they recommend inviting teammates during initial testing.
10Shifting development paradigms
Air Teams represents a maturation of agentic tools from novelty to infrastructure. By addressing team coordination, environment drift, and knowledge sharing, it enables organizations to scale AI-assisted development beyond early adopters. The platform's design acknowledges that the hardest problems in software engineering are often human, not technical.
Original article: The JetBrains Blog — JetBrains Air Teams: Agentic Workflows for Engineering Teams