Dissecting AI

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FDE-Gym: The Last Mile of AI Delivery

Dongxu Yang, Hongda Yan, Renxin Wang, Baoli Zhang, Haojia Zuo, Ying Zhao et al.

arXiv 2610.22350 14 pages Read the paper

The short version

The problem
AI agents can do well when told exactly what task to complete, but customer projects often start with a vague or misleading request. Engineers must find out what the customer really needs, navigate existing systems and people, and avoid changes that make things worse.
The idea
FDE-Gym is a practice and testing environment built from realistic customer projects. An agent can inspect a company’s files and systems, ask scripted customer staff questions, and make changes; its score reflects whether the customer’s real-world measure improved, while accounting for the cost of asking and operational mistakes. For example, a request for a chat tool might conceal a more important problem in an existing business process.
What it got
Across 13 tested AI configurations, the best earned an average score of 0.56 on a scale where 1 matches an expert-built solution. It fully resolved only 14 of the benchmark’s 95 tasks.

Modules from this paper

FDE-Gym: Business-Outcome Scoring

Deployment scores can hide business harm and costly customer interactions; FDE-Gym compares improvement with an expert reference, then adjusts for incidents and contact costs.

evaluationbusiness-outcomesdeploymentagent-systems

FDE-Gym’s Online Practice Platform

People and agents need a comparable way to practice customer delivery; FDE-Gym puts them in shared simulated workspaces with common stakeholders and graders.

AI agentsevaluationhuman-agent collaborationsimulation

Scripted Stakeholders and Anti-Gaming Task Validation

Customer-delivery tasks are hard to reproduce and easy to game, so FDE-Gym fixes stakeholder behavior and admits tasks only when predefined anchors test meaningful success and shortcut resistance.

evaluationagentsbenchmark-designreproducibility

FDE-Gym: Constructing Customer-Delivery Tasks

Real delivery projects hide both the problem and its constraints; FDE-Gym turns them into shared-environment variants and instruction levels that reveal what an agent can infer.

AI agentsbenchmarkscustomer deliveryevaluation