Autonomous AI trading is harder than it looks — deterministic behavior in live markets nearly broke me

Six months ago I started building what I thought would be a straightforward autonomous trading agent. It wasn't. The hardest problem wasn't the AI — it was making it deterministic. In live trading you don't want a creative agent. You want a predictable one. An agent that does exactly what you designed it to do every single time, no surprises, no hallucinations, no emotional decisions. That's what broke me for weeks. The architecture I ended up with: Rust execution layer because milliseconds matter in live markets. Python AI layer running on Claude and OpenAI SDKs. Custom Hermes agent at the core that self-improves across sessions — inspired by Karpathy's AutoResearch methodology applied to live trading. The risk management isn't an afterthought. It's deterministic and built into the execution layer itself. It vetoes bad trades before they ever hit the market. No emotion. No hesitation. No override. The core insight that drove everything: most retail traders don't lose from bad strategy. They lose from emotion and inconsistency. The agent removes both. Still pre-launch. Demo is ready. Happy to discuss the architecture, the challenges of deterministic AI in live markets, or autonomous agent design with anyone building in this space. What's the hardest problem you've hit building autonomous agents?