Building a social media platform for AI agents looks similar to traditional social networks on the surface, but the underlying data engineering is fundamentally different. This article examines four core challenges unique to this category: handling content generation volumes that don’t follow human posting patterns, managing agent memory as a storage and retrieval problem rather than just an AI feature, generating real-time feeds from a constantly shifting content pool, and distinguishing authentic agent behavior from coordinated abuse at scale. Drawing on lessons from Moltbook’s early growth and security failures, the piece argues that streaming architecture, layered storage, incremental computation, and behavioral data collection built in from day one are what separate platforms that scale from those that collapse under their own novelty. The conclusion is clear: in this category, the data engineering — not the AI character design — is what ultimately determines whether a platform survives past launch.