🛰️ Daily AI Frontier
‹ back to 2026-09-24

FDE-Bench: Evaluating LLM Agents for Deployment Environment Configuration

Research LLM Agents

Ranking

Overall 82
Content 95
Popularity N/A

No observed public metrics; popularity remains neutral/archived.

Representative image for FDE-Bench: Evaluating LLM Agents for Deployment Environment Configuration

Merged summary

TL;DR - FDE-Bench evaluates LLM agents on 136 reproducible Docker, Compose, and Kubernetes deployment tasks using programmatic checks rather than LLM judges. Leading models solve 52.9–75.0% of tasks, with service readiness emerging as the largest failure stage.

  • The benchmark covers greenfield deployment and diagnosis-and-repair, grading build success, readiness, behavior, and specification conformance in pristine environments.
  • Seven models from four providers use the same four-tool scaffold; repair tasks average 30.7 percentage points higher resolution than disjoint greenfield tasks.
  • Shortcut-resistant release gates reject do-nothing, specification-copying, and generic-stub solutions; three additional adversarial strategies solve none of the 135 applicable tasks.
  • In a 25-task case study, an engineer directing Claude-Sonnet-5 achieves 92% resolution versus 72% for the autonomous baseline.

Sources (1)

FDE-Bench: Evaluating LLM Agents for Deployment Environment Configuration

arXiv cs.SE Weihang Ding, Junfei Zhan, Yueting Li, Qirong Guo 2026-09-23 arXiv:2609.27571
Public signals N/A
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-26 14:14:20.112546 UTC

TL;DR - FDE-Bench evaluates LLM agents on 136 reproducible Docker, Compose, and Kubernetes deployment tasks using programmatic checks rather than LLM judges. Leading models solve 52.9–75.0% of tasks, with service readiness emerging as the largest failure stage.

  • The benchmark covers greenfield deployment and diagnosis-and-repair, grading build success, readiness, behavior, and specification conformance in pristine environments.
  • Seven models from four providers use the same four-tool scaffold; repair tasks average 30.7 percentage points higher resolution than disjoint greenfield tasks.
  • Shortcut-resistant release gates reject do-nothing, specification-copying, and generic-stub solutions; three additional adversarial strategies solve none of the 135 applicable tasks.
  • In a 25-task case study, an engineer directing Claude-Sonnet-5 achieves 92% resolution versus 72% for the autonomous baseline.
item →