A recent post on the Hugging Face blog walks through an experiment in which an AI agent built a 3D Paris gallery by chaining two Spaces. Instead of writing a monolithic program, the agent called one Space to generate or process part of the scene, then passed the result to a second Space to complete the gallery. The author, mishig, frames this as a demonstration of how agents can leverage the existing ecosystem of hosted machine learning tools.
The key idea is modularity: each Space performs a focused task, and the agent acts as the glue that connects them. This approach avoids the need for custom integration code and shows how complex outputs can emerge from simple, reusable components. The post is a single source, so there are no conflicting accounts; it is best read as a proof-of-concept for agent-driven workflows on Hugging Face.
For practitioners, the takeaway is that Spaces can serve not just as standalone demos but as composable units in a larger pipeline. The Paris gallery is a toy example, but the same pattern could apply to more serious applications, such as multi-stage data processing or multimodal content generation. The post does not claim this is a production-ready method, but it offers a clear illustration of the direction agentic tooling is heading.