FDE-Gym: The Last Mile of AI Delivery

Dongxu Yang, Hongda Yan, Renxin Wang et al.1 min readarXivOct 15, 2026

Key Takeaway for Students

Existing agent benchmarks typically evaluate agents on well-specified tasks defined by issue reports, specifications, or task tickets. Real-world customer delivery is different: forward-deployed engineers (FDEs) must clarify ambiguous requirements, work in live systems, and uncover unstated constraints. We introduce FDE-Gym, a benchmark of 95 end-to-end delivery tasks from 20 c

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This paper introduces a novel benchmark that addresses the critical gap between typical agent evaluations and real-world AI delivery challenges. It highlights the need for agents to handle ambiguity and operate in complex, live systems, pushing the boundaries of autonomous AI deployment.

Read original on arXiv

Career Insight

high demand

Why it matters

Skills to learn

Designing agents for ambiguous, open-ended tasksDeveloping agents that interact with live systems and APIsTechniques for evaluating agent performance in complex, multi-stakeholder environmentsStrategies for integrating AI agents into existing customer workflowsUnderstanding and mitigating risks in deploying AI agents to production environments

Interview questions

Describe the key differences between FDE-Gym and traditional agent benchmarks like ALFWorld or WebArena. What new capabilities does FDE-Gym aim to evaluate?
How would you design an agent architecture to effectively handle ambiguous requirements and unstated constraints, as described in the FDE-Gym context?
Propose a system architecture for an AI-powered FDE that can interact with diverse customer systems (e.g., databases, APIs, UIs) and stakeholders, while ensuring security and reliability in a production environment.

Relevant for ~24 months · generated by the enrichment pipeline