Stop Burning Tokens: Start With Domain Experts
AI Engineer · youtube · 2026-07-19
This talk focuses on applying a self-improvement loop to a paper classification task. Using a dataset with ground truth, the author demonstrates that for these methods to be truly effective, the key isn't just letting agents optimize themselves. Instead:
- The task must be sufficiently narrow and precisely measurable.
- The objective function must be high-quality; otherwise, the loop simply amplifies noise.
- The most effective systems require domain experts and automated systems to hand off tasks at different stages.
The core takeaway: don't blindly burn tokens chasing general self-optimization. First, solidify your problem definition and evaluation criteria.
Related event: Agent Harness Self-Improvement and Domain-Specific Design(6 posts)→
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