Achieving concurrency with AI subagents is a significant step toward faster and more efficient development cycles. Both Cursor and Gemini CLI provide ways to handle complex tasks by delegating them to multiple subagents.

Multiple Subagents in Cursor CLI

Cursor CLI has deep support for parallel execution of subagents. This allows you to break down a large feature or refactor into smaller, independent sub-tasks that can be worked on simultaneously. You can read more in the official subagents documentation.

• Parallel Subagents: Cursor 2.4+ allows you to spawn autonomous subagents that work in parallel. Each subagent maintains its own isolated context window, preventing the main conversation from becoming cluttered.
• Git Worktree Integration: To avoid file system conflicts, Cursor leverages Git worktrees. Each concurrent agent runs in its own worktree, allowing them to edit, build, and test code without interfering with other agents.
• Independent Loops: Subagents run their own tool-calling loops (researching, terminal commands, etc.), only returning to the main agent when their specific task is complete.

Concurrency in Gemini CLI

While native subagent tool calls in Gemini CLI are currently sequential by default, there are several ways to achieve true parallel execution.

• Agent Development Kit (ADK): The ADK provides a ParallelAgent class. You can compose multiple subagents under a ParallelAgent, which then manages their concurrent execution and aggregates their findings.
• Spawning CLI Instances: A common pattern is using the shell tool within Gemini CLI to launch new gemini-cli instances for sub-tasks. By running these instances in “YOLO mode” (--yolo), they can auto-approve tools and operate independently in parallel processes.
• Remote Subagents: Gemini CLI supports the Agent-to-Agent (A2A) protocol, allowing it to delegate work to remote subagents across the network.
• Containerization: For high-trust environments, subagents can be spawned in separate containers, providing both concurrency and strict environment isolation.

By leveraging these patterns, you can signicantly reduce the wall-clock time required for complex AI-driven code migrations and research tasks.