Unlocking the Potential of AI Coding Agents 🚀
For months, I’ve immersed myself in developing tools for AI coding agents. I hit a pivotal challenge that sparked my curiosity:
- Observation Saving Issue: When trying to instruct agents (like Claude Code, Cursor, and Codex) to save insights for future sessions, they only comply about 30% of the time.
- Current vs. Future Tasks: These models prioritize task completion in the present over future benefits, which creates a misalignment in their incentive structures.
My solution? A passive observation system that captures agent behaviors and infers insights without needing cooperation. But I’m eager to hear from the community!
Join the conversation! Have you found techniques that prompt agents to self-document effectively? Here are a few areas to explore:
- Prompt structures for context-saving
- Fine-tuning for knowledge retention
- Alternative architectures for memory
🔗 Let’s collaborate—share your experiences and insights below!
