Agents should fit their harnesses to data, not hand-code the workflow layer
BraceSproul · x · 2026-07-23
The post argues that agent harnesses should be learned from data, not hand-designed.
- Just as neural network weights are discovered by fitting to data, the harness around an agent should be optimized from real workflow data.
- As more intelligence moves in-house, the harness becomes more valuable because it encodes business-specific details.
- The suggested process is careful data curation, auto-research, a holdout set, and human review.
- The underlying claim is that this makes agents more token-efficient and generally better.
More from coding & agent
- One prompt, full Blender animation: dev says he never touched Blender once — iamfakhrealam · 2026-10-26
- Claude Code finds root backdoor in Honda Civic head unit, then debloats it — IndraVahan · 2026-10-26
- Claude Code + Monid MCP builds a $12K/year B2B prospecting engine on live data — aryanXmahajan · 2026-10-26
- Karpathy shares CLAUDE.md rulebook to stop AI coding agents from piling up tech debt — mdancho84 · 2026-10-26
- Rethinking dependencies in the agent era: drop libs that save 20 lines of code — gethackteam · 2026-10-26
- RustGIS hits 2,000 GitHub stars in two weeks with agent-first architecture — giswqs · 2026-10-26