Objectives: This study evaluated the feasibility and performance of a multi-agent (MA) system designed to support early sepsis management in intensive care units. The system integrates three specialized agents—sepsis management, antibiotic recommendation, and guideline compliance—to provide evidence-based recommendations at T = 0 hours (before culture results), extending prior single-case findings across 10 diverse cases. Methods: The MA system was powered by Palmyra- Med 70B (selected for superior MedQA performance [average score, 85.9]) and compared with GPT-3.5 Turbo and GPT-4o mini (all at a temperature of 0.25). It used retrieval-augmented generation (RAG) with ChromaDB (2021 Surviving Sepsis Campaign, over 20 high-impact manuscripts [reviews published 2018–2025] on sepsis etiologies, and other relevant sources). Eight cases from the MIMIC-IV demo and two cases from the literature were formatted as vignettes. RAG used the BAAI/bge-base-en-v1.5 embedding model with cosine similarity (threshold, 0.75) and top-5 chunks. Performance was assessed via TruLens (groundedness, approximately 0.62) and by two intensivists using a standardized questionnaire. Results: The system generated guideline-compliant recommendations (e.g., prompt surgical debridement plus meropenem and vancomycin for necrotizing fasciitis). Hallucinations occurred in three of 10 cases (e.g., “altered mental status”). Expert agreement was quantified by a Cohen kappa of 0.26. Programmatic and expert assessments showed negligible correlation. Conclusions In this exploratory study, the MA system shows preliminary promise for early sepsis support but requires human oversight to mitigate hallucinations. Code is available in GitHub; further validation is needed.

Objectives: The high incidence of sepsis necessitates the development of practical decision-making tools for intensivists, especially during the early, critical phases of management. This study evaluates a multi-agent system intended to assist clinicians with antibiotic therapy and adherence to current sepsis management guidelines before diagnostic results become available. Methods: A multi-agent system incorporating three specialized agents was developed: a sepsis management agent, an antibiotic recommendation agent, and a sepsis guidelines compliance agent. A sepsis case from the MIMIC IV database, organized as a clinical vignette, was used to integrate and test these agents for generating management recommendations. The system leverages retrieval-augmented generation to improve decision-making through the integration of current literature and guidelines. Results: The application produced management recommendations for a sepsis case associated with pneumonia, including early initiation of broad-spectrum antibiotics and close monitoring for clinical deterioration. Two expert intensivists evaluated these recommendations as “acceptable” and reported moderate interrater agreement (Cohen’s kappa = 0.622, p = 0.003) across various aspects of recommendation usefulness. Conclusions The multi-agent system shows promise in enhancing decision-making for sepsis management by optimizing antibiotic therapy and ensuring guideline compliance. However, reliance on a single case study limits the generalizability of the findings, highlighting the need for broader validation in diverse clinical settings to improve patient outcomes.

Objectives: The high incidence of sepsis necessitates the development of practical decision-making tools for intensivists, especially during the early, critical phases of management. This study evaluates a multi-agent system intended to assist clinicians with antibiotic therapy and adherence to current sepsis management guidelines before diagnostic results become available. Methods: A multi-agent system incorporating three specialized agents was developed: a sepsis management agent, an antibiotic recommendation agent, and a sepsis guidelines compliance agent. A sepsis case from the MIMIC IV database, organized as a clinical vignette, was used to integrate and test these agents for generating management recommendations. The system leverages retrieval-augmented generation to improve decision-making through the integration of current literature and guidelines. Results: The application produced management recommendations for a sepsis case associated with pneumonia, including early initiation of broad-spectrum antibiotics and close monitoring for clinical deterioration. Two expert intensivists evaluated these recommendations as “acceptable” and reported moderate interrater agreement (Cohen’s kappa = 0.622, p = 0.003) across various aspects of recommendation usefulness. Conclusions The multi-agent system shows promise in enhancing decision-making for sepsis management by optimizing antibiotic therapy and ensuring guideline compliance. However, reliance on a single case study limits the generalizability of the findings, highlighting the need for broader validation in diverse clinical settings to improve patient outcomes.

Objectives: The high incidence of sepsis necessitates the development of practical decision-making tools for intensivists, especially during the early, critical phases of management. This study evaluates a multi-agent system intended to assist clinicians with antibiotic therapy and adherence to current sepsis management guidelines before diagnostic results become available. Methods: A multi-agent system incorporating three specialized agents was developed: a sepsis management agent, an antibiotic recommendation agent, and a sepsis guidelines compliance agent. A sepsis case from the MIMIC IV database, organized as a clinical vignette, was used to integrate and test these agents for generating management recommendations. The system leverages retrieval-augmented generation to improve decision-making through the integration of current literature and guidelines. Results: The application produced management recommendations for a sepsis case associated with pneumonia, including early initiation of broad-spectrum antibiotics and close monitoring for clinical deterioration. Two expert intensivists evaluated these recommendations as “acceptable” and reported moderate interrater agreement (Cohen’s kappa = 0.622, p = 0.003) across various aspects of recommendation usefulness. Conclusions The multi-agent system shows promise in enhancing decision-making for sepsis management by optimizing antibiotic therapy and ensuring guideline compliance. However, reliance on a single case study limits the generalizability of the findings, highlighting the need for broader validation in diverse clinical settings to improve patient outcomes.