A multi-agent conversational artificial intelligence ecosystem for real-time integration of clinical, genomic, and social determinants of health data in colorectal cancer precision oncology

Introduction: The rapid expansion of clinical, genomic, and social determinants of health (SDoH) data has outpaced traditional analytic approaches, creating an urgent need for intelligent systems capable of integrating and interpreting complex oncology datasets at scale. To address this challenge, our team developed a suite of domain-specific conversational AI agents that enable real-time, natural language-driven exploration of multi-omic colorectal cancer (CRC) data. These agents facilitate discovery of population-specific molecular alterations and treatment-response patterns. Methods: Each agent leverages fine-tuned biomedical LLaMA-3 models, a natural language-to-code interpreter, and a backend statistical engine linked to harmonized datasets from TCGA, AACR Project GENIE, and cBioPortal. The platform automates cohort creation, mutation profiling, survival analysis, odds ratio testing, and integration of clinical and SDoH variables. Specialized agents include AI-HOPE-PI3K, TGFbeta, TP53, RTK-RAS, JAK-STAT, MAPK, WNT, and AI-HOPE-PM, the latter uniquely integrating clinical, genomic, and SDoH features. A core agent maintains data interoperability across the ecosystem. All analyses are triggered by plain-language prompts and return visual and narrative outputs within seconds. Results:AI-agents successfully reproduced established clinical-genomic associations and uncovered novel, clinically meaningful insights. AI-HOPE-PI3K identified INPP4B mutations enriched in Hispanic/Latino early-onset CRC; AI-HOPE-TGFbeta detected MSI-associated survival benefits in SMAD4-mutant tumors; and AI-HOPE-PM revealed worse survival in TP53-mutant CRC cases experiencing financial strain, along with differences in chemotherapy access linked to food insecurity. Additional agents identified prognostic variation across stage, treatment exposure, and demographic groups within MAPK, RTK-RAS, and WNT pathways. Accuracy exceeded 90% across use cases, with all analyses completed in real time without requiring programming expertise. Conclusions: Multi-agent conversational artificial intelligence ecosystem provides a scalable, interoperable, and population-informed multi-agent architecture for precision oncology. Development of Agent-to-Agent (A2A) and Modular Collaborative Protocols (MCPs) will enable coordinated, cross-domain analysis and hypothesis generation, advancing a collaborative AI ecosystem for cancer research. By unifying clinical, molecular, and SDoH data under a conversational interface, AI-agents ecosystem introduces a new paradigm for data intelligence, accelerates biomarker discovery, and supports population-informed precision oncology across populations.