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

• URL: http://arxiv.org/abs/2511.15755v1
• Date: Wed, 19 Nov 2025 06:06:11 GMT
• Title: Multi-Agent LLM Orchestration Achieves Deterministic, High-Quality Decision Support for Incident Response
• Authors: Philip Drammeh,
• Abstract summary: Large language models (LLMs) promise to accelerate incident response in production systems.<n>Yet single-agent approaches generate vague, unusable recommendations.<n>We present MyAntFarm.ai, a containerized framework demonstrating that multi-agent orchestration transforms incident response quality.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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