Stop Burning Tokens: Why self-improvement needs domain expertise first - Annabell Schäfer, Langfuse

We ran auto-improvement loops on a paper classification task against a ground-truth dataset. A real problem, narrow enough to measure precisely, and in fact one of the few clear cut target functions out there.

We’ll share how to properly set up an agent for auto-improvement, what task specificity and target function quality is actually required for it to work, and why the most efficient path to a continuously improving agentic system is one where domain experts and automation know when to hand off to each other.​​​​​​​​​​​​​​​​

Speakers:
- Annabell Schäfer (Langfuse): Annabell is a Growth Engineer at Langfuse, the largest Open Source AI observability and evaluation platform. She is passionate about building cutting edge AI systems and has been around in the space since 2022.
  X/Twitter: https://x.com/annabellschfr
  LinkedIn: https://linkedin.com/in/annabell-schaefer/
  GitHub: https://github.com/annabellscha