EcoAgent-Bench: Evaluating Economic Decision-Making in Budget-Constrained LLM Agents

• URL: http://arxiv.org/abs/2608.05519v1
• Date: Thu, 06 Aug 2026 01:47:47 GMT
• Title: EcoAgent-Bench: Evaluating Economic Decision-Making in Budget-Constrained LLM Agents
• Abstract summary: We introduce EcoAgent-Bench, in which every task specifies priced actions and an explicit budget.<n>Our results show that completion under a budget and economical action selection are distinct properties.
• Score: 8.799203536862382
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agent benchmarks usually measure task completion and treat resource use as an auxiliary statistic. In deployment, however, the choice among a local lookup, broad search, composite research tool, stronger model, or human escalation is part of the task itself. We introduce EcoAgent-Bench, in which every task specifies priced actions and an explicit budget. Its 304 real-derived tasks span five families adapted from GAIA, HotpotQA, and MuSiQue, and test four decisions: avoiding unnecessary escalation, escalating when local evidence is insufficient, selecting a model tier, and stopping on unsupported premises. We evaluate seven LLM agents in tool-API and workspace-CLI settings, together with four oracle scripted controls. Micro-averaged accuracy rewards one-sided policies: always-escalate controls achieve high micro success while failing save-oriented tasks. We therefore also report an economic-consistency score (the worse of accuracy on upgrade-oriented and save-oriented family groups) which exposes this failure. Tool-API agents attain only 3.9-24.0% micro strict success (at most 7.3% economic consistency), often either stopping before warranted escalation or overspending on cheap tasks. A threshold-crossing budget sweep changes GPT-5.4's escalation rate from 0% to only 3%. These results show that completion under a budget and economical action selection are distinct properties. We release the task bundle, transformation pipeline, frozen evaluation environments, and integrity-bound result artifacts needed to study both.

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