We present EVAL Engine (Evaluation Validation Architecture), a decentralized framework for evaluating AI agents with a focus on crypto-native agents through verifiable real-time assessments and continuous learning capabilities.

Our system utilizes Chromias gas-free relational blockchain architecture to enable transparent, immutable, and cost-effective evaluation of AI agent performance. The system incorporates multiple LLM-as-a-judge[1] and social engagement metrics for continuous reinforcement learning via feedback loop and reward system.

We demonstrate EVAL Engine can achieve efficient, secure evaluations while adapting to evolving performance standards through engagement-driven feedback loops.

We also present a comprehensive roadmap for the development of EVAL Engine, including API development, data preparation, model development, and model deployment.