[kayba]

You're right that cost and latency are important considerations. However, the research shows this isn't just about finding better templates, it's about enabling agentic systems to learn and improve from their previous attempts and failures.

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

[jimmySixDOF]

I did look into DataRobot's Syftr which points at the same problem but is a lot heavier I definitely like that the approach you take is at least easy to get a basic version up and can start checking the results right away!