Fig. 3: Multi-agent network problem-solving architecture for complex clinical scenarios.

Comprehensive diagram illustrating how AI agent networks solve sophisticated clinical problems through distributed intelligence and collaborative reasoning. Starting with complex clinical presentation (45-year-old with Met-ALD and suspected malignancy), the system dynamically forms appropriate network topology progressing from simple (2–3 agents) through standard (4–8 agents) to complex swarm configurations (10+ agents) based on case complexity assessment. Parallel specialized analysis shows four primary agents: Metabolic Assessment Agent identifying MASLD risk factors, Alcohol History Agent detecting moderate ALD pattern, Imaging Analysis Agent recognizing mixed HCC/iCCA features, and Pathology Integration Agent requesting additional immunohistochemistry markers. Inter-agent communication protocols demonstrate structured messaging where agents share evidence with quantified confidence levels, triggering consensus-building mechanisms when uncertainty exceeds predetermined thresholds. The emergent solutions level demonstrates network-level intelligence producing integrated treatment approaches combining metabolic optimization, alcohol cessation protocols, surgical evaluation, and molecular targeting—solutions no single agent could independently generate.