How it works: Agents execute tasks, reflect on what worked/failed, and curate a "playbook" of strategies. All from execution feedback - no training data needed.
Happy to answer questions about the implementation or the research!
We believe in-context learning is one of the missing pieces to make agentic systems feasible in production. The key is that systems can adapt without expensive fine-tuning or retraining. The paper shows *86.9% lower adaptation latency* and significant reductions in rollout costs compared to existing methods, making this approach more practical than previous optimization techniques.
The real value is in systems that progressively get better at their tasks through experience, not just one-time prompt optimization.
If you want to continue this conversation just hit me up on Discord: https://discord.com/invite/mqCqH7sTyK