🛰️ Daily AI Frontier
‹ back to 2026-09-18

How Do Agent Harnesses Create Value? Planning Information and Release Control in Stateful LLM Agents

arXiv cs.AI LLM Agents Yukun Zhang, Kemu Xu, Yishen Chen 2026-09-17

TL;DR - This study measures how planning guidance and completion verification improve stateful LLM agents. Task-specific plans raise success, while inexpensive standalone verification delivers most of the protection against false passes.

  • Fixed plans improved oracle-verified success by 7.17 percentage points across 265 matched cells, with larger gains on complex tasks.
  • A read-only terminal verifier rejected 61% of oracle-invalid Retail episodes but also withheld 17% of correct episodes.
  • Verification added less than $0.01 per episode, and its value increased when erroneous acceptance carried greater liability.
  • At high false-acceptance costs, the standalone verifier captured nearly all the full planning-plus-verification stack’s false-pass benefit at substantially lower cost.

view merged work →