Self-Evolving AI System with Full Observability Built an autonomous AI platform that can discover and integrate new capabilities at runtime. Key technical points: Event-driven core: NATS message bus handles tool discovery, registration, and execution. System observes its own operations and adapts. Self-improvement loop: AI agents can create and deploy new tools/agents to extend system capabilities—"AI building AI" without human intervention. Full transparency: Real-time visibility into decision trees, reasoning chains, and inter-agent communication (rare in production AI systems). Production-ready stack: Docker isolation, Redis for state, K8s orchestration, REST APIs. News feeds trigger autonomous goal generation. The interesting bit: Unlike typical agent frameworks that are statically configured, this learns new domains by spinning up specialized sub-agents and tools dynamically. Zero-config onboarding of new capabilities. Trade-offs worth discussing: Event-driven complexity vs. debuggability, autonomous evolution vs. drift/instability, observability overhead at scale. Help welcome: https://github.com/stevef1uk/artificial_mind.git