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The thesis: the harness is the lever

Veyyon exists because the same model weights score wildly differently depending on the agent harness around them. The model is capable; scaffolding restricts or unlocks it.

The evidence

Benchmarks and production traces show tool shape, edit shape, context handling, and control flow can swing outcomes dramatically. Veyyon’s oh-my-pi lineage adds hashline editing, role-based models, compaction, and explicit modes (plan, goal, vibe) as harness levers, not only prompt text.

The two dominant levers

  1. Edit format / first-attempt edits. When the edit format is hard to emit, models burn turns on retries. Hashline and model-tuned edit prompts are the biggest swing in Veyyon.
  2. Control flow. Stop when verification passes; do not loop on repeated failures; budget context and subagent fan-out. Goal and plan modes encode some of this in the engine.

What this implies for the design

  • Hashline and native edit tools as the primary write path in packages/coding-agent (see engine docs under docs/).
  • Per-model and per-role configuration via modelRoles, thinking levels, and catalog selectors.
  • Engine-enforced modes (plan file + approval, goal continuation, tool approval tiers).
  • Evidence discipline. Claims in this book must match tests and engine docs, or be labeled Spec — not shipped.

Harness improvements are runtime work

Dogfood traces, benchmark failures, and user corrections drive small runtime improvements: better tool hints, compaction, cache-stable prefixes, clearer progress, and bounded outputs. Name the lever each change moves.

Where to go next