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.
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