Cursor 2.0’s parallel agents feature allows multiple AI models to work simultaneously on the same project, enabling developers to generate and compare different solutions quickly for improved productivity and quality. Demonstrated through building a weather app, this approach showcases efficient parallel development, rapid iterative improvements, and diverse design outputs, despite increased costs.

The video introduces Cursor 2.0’s new feature of parallel agents, which allows multiple agents to work simultaneously on the same prompt. This approach is particularly useful for medium to large-sized projects, as it enables developers to generate different versions of a solution in parallel and then compare them to select the best outcome. The presenter demonstrates this by building a simple weather app, starting with a single agent creating a plan to ensure alignment before spinning up multiple agents to execute the plan.

In the demonstration, three different agents are launched in parallel: Composer 1, Sonnet 4.5, and GPT-5 Codex. Each agent uses a slightly different model, resulting in varied outputs despite working from the same initial plan. The interface in Cursor 2.0 makes it easy to manage these agents as they work on different branches simultaneously, providing a clean and efficient workflow for parallel development.

The presenter reviews the outputs from each agent in editor mode. Sonnet 4.5 produces a functional weather app but with a design that feels somewhat typical and heavy, featuring large rounded corners and drop shadows. GPT-5 Codex delivers a clean and appealing design that accurately displays the current temperature and weather at the user’s location. Composer 1’s version is lighter and visually pleasing, showing a different stylistic approach.

Next, the presenter adds a dark mode feature with a toggle switch for users to switch between light and dark themes. This change is implemented quickly and smoothly by the Composer 1 agent, showcasing the speed and efficiency of Cursor 2.0’s parallel agent system. The presenter expresses enthusiasm about how fast and responsive the agents are when making iterative improvements to the app.

Finally, the presenter notes that while running multiple agents in parallel increases costs, the productivity gains for complex and critical builds justify the expense. Overall, Cursor 2.0’s parallel agents feature is seen as a significant advancement in software development, enabling faster, more flexible, and higher-quality builds by leveraging the strengths of multiple AI models working side by side.