One of the biggest challenges we faced was designing a system where AI trading agents could process real-time economic news, make context-aware decisions, and execute trades—all while allowing user-level personalization. Integrating LLMs with a live news feed required more than just prompt engineering; we needed a Retrieval-Augmented Generation (RAG) architecture that could be updated frequently without retraining the model.

Another hurdle was architecting the subscription-based marketplace. Unlike one-time purchases, our model needed time-sensitive access control to agents and seamless ETH-based payments with renewal logic. We had to carefully design the smart contract to handle subscription lifecycles and revenue flow between agent creators and the platform.

Finally, managing AI agent customization via natural language (instead of code) added a layer of complexity. We had to balance user-friendliness with technical flexibility, ensuring users could shape agent behavior without breaking the agent pipeline. We solved this by structuring prompt templates and embedding user materials through vector search, enabling true no-code personalization.