aiqbench analysis
This research isolates the specific value drivers of agent harnesses, demonstrating that structured planning guidance significantly boosts success rates on complex tasks while read-only verifiers effectively filter erroneous outputs at minimal cost. The study provides a clear trade-off analysis showing that the optimal balance between planning and verification depends entirely on the system's liability for false acceptances. For developers building stateful LLM agents, these findings offer a practical framework for optimizing reliability without incurring prohibitive computational overhead.
How Do Agent Harnesses Create Value? Planning Information and Release Control in Stateful LLM Agents
arXiv:2609.20474v1 Announce Type: new 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 $\tau^2$-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.
Title:How Do Agent Harnesses Create Value? Planning Information and Release Control in Stateful LLM Agents
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 $\tau^2$-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.
Artificial Intelligence (cs.AI)
arXiv:2609.20474 [cs.AI]
(or arXiv:2609.20474v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.20474 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 261 words
