This is fascinating! The "evolving playbook" approach resonates with challenges we've been tackling building an AI agent for Django development.
A few questions about your implementation:
1. How do you handle the balance between delta updates and full context rewrites when the playbook grows large? We've found that keeping detailed history helps with debugging but can bloat context quickly.
2. The Generator/Reflector/Curator separation is elegant. Did you implement these as separate LLM calls or different prompting strategies on the same model? We use a similar dual-agent pattern (planner + executor) and the coordination overhead is non-trivial.
3. Most interesting part: "natural execution feedback without labeled supervision." How do you define success/failure signals for the Reflector in ambiguous cases? For code generation, it's easy (tests pass/fail), but for other domains it seems trickier.
The +10.6% improvement on agent tasks is impressive - definitely checking out the paper. The brevity bias problem you mention is real - we've noticed agents dropping important context details when trying to "summarize efficiently."