A Multi-Agent Large Language Model Architecture for Holistic Multi-Drug Reasoning and Medication Safety Networks

Background: Traditional clinical decision support systems (CDSS) often rely on rigid, rule-based databases that generate excessive alert fatigue and fail to account for holistic clinical contexts, such as complex patient comorbidities, cascading drug-drug-disease interactions, and polypharmacy management. The rapid evolution of Large Language Models (LLMs) offers a transformative paradigm; however, single-agent LLMs frequently suffer from hallucinations, limited multi-step reasoning capabilities, and a lack of reproducible consistency required for high-stakes clinical workflows. Methods: We propose the "Galen Engine" framework, a novel, multi-agent LLM architecture explicitly engineered for advanced medication intelligence and patient safety. The system decomposes complex clinical reasoning into specialized, autonomous agents: an Anamnesis & Extraction Agent to contextualize patient history; a Pharmacokinetic/Pharmacodynamic (PK/PD) Modeling Agent to simulate multi-drug behaviors; an Interaction & Toxicity Risk Agent; and a Synthesizer Agent that cross-examines outputs using verified medical knowledge graphs (e.g., RxNorm, Mayo Clinic Knowledge Base). To address hallucination and enforce safety, we implement a Retrieval-Augmented Generation (RAG) pipeline combined with a deterministic verification layer. System reproducibility and stability were rigorously benchmarked across diverse multi-drug clinical scenarios using Coefficient of Variation (CV) metrics. Results: Preliminary evaluations demonstrate that the multi-agent orchestration significantly reduces critical clinical hallucinations compared to baseline single-agent LLM deployments. The architecture achieves high granular sensitivity in identifying indirect drug-disease-supplement interactions while maintaining an ultra-low intra-scenario CV, demonstrating robust consistency across iterative inference cycles. Crucially, the system mitigates alert fatigue by contextually filtering non-relevant warnings, delivering concise, actionable risk stratifications rather than binary alerts. Conclusion: The proposed multi-agent framework provides a scalable, context-aware architecture for the next generation of medication safety systems. By combining the probabilistic reasoning of LLMs with deterministic clinical guardrails, the system establishes a safe, complementary framework where the AI highlights systemic pharmacological risks, while final diagnostic and therapeutic autonomy remains strictly within the clinician's jurisdiction.