Imagine asking Spotify to “play some fresh indie soul I might like” and then, mid-session, saying “I love this song, tell me more about this artist.” Conversational recommendation agents make interactions like this possible: users can express what they want in natural language, then follow up, refine, or change their requests as the conversation unfolds.

Building such an agent comes with a practical challenge: before launch, there is no real multi-turn conversational data. Without users interacting with the feature, we cannot observe how conversations unfold, where the agent fails, or which capabilities need the most work.

In this post, we describe how we tackled this cold-start problem at Spotify using synthetic multi-turn conversations and an automated self-improvement loop.