Large language models (LLMs) promise to accelerate incident response in production systems, yet single-agent approaches generate vague, unusable recommendations. We present MyAntFarm.ai, a reproducible containerized framework demonstrating that multi-agent orchestration fundamentally transforms LLM-based incident response quality. Through 348 controlled trials comparing single-agent copilot versus multi-agent systems on identical incident scenarios, we find that multi-agent orchestration achieves 100% actionable recommendation rate versus 1.7% for single-agent approaches, an 80 times improvement in action specificity and 140 times improvement in solution correctness. Critically, multi-agent systems exhibit zero quality variance across all trials, enabling production SLA commitments impossible with inconsistent single-agent outputs. Both architectures achieve similar comprehension latency (approx.40s), establishing that the architectural value lies in deterministic quality, not speed. We introduce Decision Quality (DQ), a novel metric capturing validity, specificity, and correctness properties essential for operational deployment that existing LLM metrics do not address. These findings reframe multi-agent orchestration from a performance optimization to a production-readiness requirement for LLM-based incident response. All code, Docker configurations, and trial data are publicly available for reproduction.

翻译：大语言模型（LLM）有望加速生产系统的事件响应，但单智能体方法往往生成模糊且不可行的建议。本文提出MyAntFarm.ai——一个可复现的容器化框架，证明多智能体编排从根本上改变了基于LLM的事件响应质量。通过对相同事件场景进行348次对照试验，比较单智能体副驾驶与多智能体系统的表现，我们发现多智能体编排实现了100%可执行建议率，而单智能体方法仅为1.7%，在行动特异性方面提升80倍，在解决方案正确性方面提升140倍。关键在于，多智能体系统在所有试验中表现出零质量方差，这使得生产环境SLA承诺成为可能——这是输出不一致的单智能体系统无法实现的。两种架构在理解延迟方面表现相近（约40秒），表明架构价值在于确定性质量而非速度。我们提出了决策质量（DQ）这一新颖指标，它捕捉了现有LLM指标未能涵盖的、对实际部署至关重要的有效性、特异性和正确性特征。这些发现将多智能体编排重新定位为基于LLM的事件响应从性能优化转向生产就绪的必备条件。所有代码、Docker配置及试验数据均已公开以供复现。