agentic_ontology

DeFi/Crypto Macro-Regime RSI Engine—an AI system that reads the economy like a team of macro strategists and explains what it means for crypto. Eight specialized AI agents continuously analyze live Federal Reserve economic data, while an orchestrator synthesizes their findings into a single market verdict. Unlike traditional LLMs, every conclusion is grounded in an ontology knowledge graph that models causal relationships, measured correlations, and historical time lags, reducing hallucinations and producing explainable market intelligence. Our Hackathon contribution extends this system with Recursive Self-Improvement (RSI). We use Claude Sonnet 4.6 as a teacher to generate high-quality reasoning traces, distill that knowledge into a compact model, and allow the student model to improve its own weights through an iterative feedback loop. Every new model is objectively evaluated and promoted only if it outperforms the current version, creating a safe, autonomous learning system. This work is important because it demonstrates a new blueprint for trustworthy AI agents: grounded by causal knowledge, governed through versioned reasoning, continuously self-improving, and composable through MCP. While we apply it to macroeconomics and crypto today, the same architecture can power AI decision-making in healthcare, supply chains, manufacturing, climate science, and any domain where reasoning must be explainable and evidence-based.