Oh man, I totally feel your frustration with those rogue agent decisions! I spent months dealing with my smart home trying to turn off my server while I was remote because it thought it was saving energy... absolute nightmare! You definitely dont need to dive into the deep end with RLHF to fix this. My favorite trick for keeping these things on track without lagging out is implementing a methodical validation layer using a small but powerful local model. I switched my setup over to Mistral AI Mistral 7B Instruct v0.3 running on a Raspberry Pi 4 Model B 8GB and the results were amazing! Instead of full chain-of-thought, I recommend a structured Negative Constraints list in your system prompt. Specifically, define a set of immutable rules that the agent must parse before generating any output. For instance, you tell the agent what it is strictly forbidden from doing, such as cutting power to essential appliances like the fridge. This makes the decision-making process much more professional and reliable. If you are worried about data quality, I found that sticking with reliable gear like the Sonoff S31 Lite Zigbee Smart Plug for monitoring makes a huge difference. When the agent has high-fidelity data, it makes better choices! You should also try a verification step where the agent must check a local state file against its proposed action before execution. It is a fantastic way to bridge that gap without making the stack super complex! It worked wonders for my Seattle setup!