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

2026-09-17

AI Summary

研究通过对比预写任务计划与随机策略文本，分析了代理约束在零售和航空实验中如何影响成功率、错误接受率及成本。

Abstract

Agent harnesses supply planning guidance, organize execution, and check completion. We study how these components affect success, erroneous acceptance, and cost in two Retail experiments and an Airline pilot in $τ^2$-bench. The primary comparison pairs prewritten task-specific plans (Fixed) with shuffled policy text matched in word count (Sham), isolating the contribution of guidance content. Across 265 matched cells, Fixed improves oracle-verified success by 7.17 percentage points (90\% task-clustered bootstrap interval, 1.15--13.36 points), with gains concentrated in higher-complexity tasks. A read-only terminal verifier rejects 61\% of Retail oracle-invalid episodes while withholding 17\% of correct ones, at less than one cent of additional cost per episode. Which component matters more depends on the loss assigned to erroneous acceptance: at low liability the planning gain dominates; at high liability the verifier's avoided false passes dominate---and a standalone verifier captures nearly all the false-pass benefit of the full planning-plus-verification stack at a fraction of its cost.

Problem

• Agent Harnesses
• Planning Information
• Release Control

Innovation

• Agent Harnesses
• Planning Information
• Release Control
• Stateful LLM Agents
• Error Acceptance