Multi-Agent LLM Orchestration Achieves Deterministic, High-Quality Decision Support for Incident Response

TL;DR

This study tackles the problem that single-agent LLMs often produce vague, non-actionable guidance during time-critical incidents. It proposes MyAntFarm.ai, a reproducible, containerized platform that compares manual analysis, single-agent prompting, and multi-agent orchestration across 348 controlled trials. The key finding is that multi-agent orchestration yields deterministic, fully actionable recommendations with zero variance, dramatically higher actionability, specificity, and correctness than single-agent approaches, while maintaining comparable comprehension latency. This work reframes LLM-based incident response as a production-readiness problem, introduces the Decision Quality metric to capture operational utility, and provides a reproducible framework for validating these results across scenarios and model scales.

Abstract

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.