• Agent Architecture: Designing an architecture where AI agents can securely control proxy accounts required careful handling of minimal proxies, initialization flows, and execution guards.
• Intent-to-Action Mapping: Translating user intents into executable transaction flows was complex, especially when supporting arbitrary dApps and custom ABIs. We created a flexible intent parser with fallback rules.
• Multi-module Communication: Since each module (e.g., user modeler, tx agent, environment fetcher) runs independently, we faced challenges around standardized inter-service communication and state sharing. We solved this using structured Express APIs and centralized gateway routing.
• AI Integration Layer: Hooking ElizaOS and local LLMs into agent execution while maintaining low latency and traceability involved customizing the runtime and optimizing embedding strategies.