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)→
More from coding & agent
- Gergely Orosz: Shipping 10x PRs With AI Agents, Sites Fill With Small Regressions — ducha_aiki · 2026-09-11
- Same Echo Maze prompt, three frontier models: all passed visually but shipped the same hidden bug — eyishazyer · 2026-09-11
- Astra storyboards plus Minimax H3 per-shot generation boost video success rates — Hailuo_AI · 2026-09-11
- Codex tip: use Sol with Astra and Luna sub-agents to save usage — pvncher · 2026-09-11
- agents-best-practices: a provider-neutral Agent Skill for designing and auditing agentic harnesses — tom_doerr · 2026-09-11
- Cognition's SWE-2 uses a KKT duality argument in RL to shift the effort Pareto curve — YouJiacheng · 2026-09-11