With the background agent, we aim to simplify the process for developers to create environments where the model can run tests and make it repeatable, allowing for quick updates as code states change.

The background agent feature allows developers to work on multiple changes simultaneously, enhancing productivity and streamlining the development process.

The next limiting factor for coding agents will be verification. While models are improving at writing code, real-world applications often lack perfect unit tests, making verification challenging.

Ambient agents allow for scalability, enabling thousands of them to run in the background, which significantly increases our impact.

The use of Cursor's Agent feature is particularly beneficial for laying out new code bases and addressing specific coding challenges.

We've invested heavily in understanding large codebases, which is crucial for making significant changes quickly.

Training retrieval models and integrating various sources of context can enhance the model's understanding of large codebases, especially by leveraging recent changes made by developers.

While giving the model access to good retrieval is important, it is still a fundamental challenge to ensure the model truly understands the codebase, which we are keen to solve.

Debugging AI agents is challenging due to the complexity of visualizing their processes, which often leads to difficulties in understanding their behavior.