Original Reddit post

I’ll start - Harness your AI Agent . For me, “harnessing” means building and integrating layers around the LLM so it has safe space to work freely, while keeping it on course and avoid risking previous work. Some patterns I’ve found especially useful: Efficient memory layer

• allows you to work in a consistent environment. Better memory architecture = conversations feel continuous and natural. Also, of course, allows the LLM to reuse context across sessions, etc. Integrate hooks to automate sub-processes - e.g., after /plan finishes - automatically trigger a sub-agent that reviews and critiques the suggested plan. This pattern have been VERY effective for me, and the final output is consistently higher-quality. A self-tuning mechanism - connect your LLM to a self-tuning or evaluation loop so it can learn from mistakes instead of repeating bad habits. This might be the single, most critical thing I’ve learned about working with AI agents: it’s how you ensure your AI is actually improving over time, not just “doing more of the same”. What’s yours? submitted by /u/sliamh21

Originally posted by u/sliamh21 on r/ClaudeCode