Metabolic syndrome is a growing global cardiometabolic burden and is increasingly accompanied by demand for continuous, personalized, and evidence-grounded nutrition support. A 2025 systematic review and modelling analysis estimated that 1.54 billion adults were living with metabolic syndrome in 2023, with global prevalence estimated at 28.4% across women and men. Conventional nutrition trackers provide structured quantitative logging, but manual food search, portion selection, and fragmented workflows can increase interaction burden. Large language models (LLMs) provide a natural conversational interface, yet unrestricted generation can produce unsupported nutritional or clinical statements. This paper presents NutriCoach Pro, an agentic nutrition-intelligence architecture that separates deterministic computation, verified food data, evidence retrieval, multimodal perception, and language generation. The system combines a Mifflin-St Jeor energy-estimation engine, activity-adjusted energy expenditure, configurable macronutrient targets, hydration tracking, a bounded Health Compliance Score, deterministic multi-item food parsing, multimodal meal recognition, a sevendomain clinical knowledge base, local retrieval-augmented generation (RAG), optional cloud-model adapters, schema-controlled database actions, and WebSocket-based real-time synchronization. A prototype evaluation plan is defined for NLP extraction, calorieestimation error, unsupported clinical claims, latency, multimodal recognition, and user task performance. Project-reported prototype figures supplied for this manuscript are F1=96.4%, mean absolute calorie error=12.3 kcal, local-RAG latency=42.1 ms, WebSocket synchronization latency=28.4 ms, and unsupported-claim rate below 1.2%; these values are explicitly treated as provisional until reproduced with a documented held-out test protocol. The contribution is an explainable, modular, privacy-oriented architecture that places hard numerical and safety boundaries around generative Al rather than treating an LLM as the source of truth or as an autonomous Clinical decision maker

Kubera B, Sharath S R, Anoushka, Lavanya S U, Maseeha Banu, "Nutricoach Pro: An Agentic Real-Time Retrieval-Augmented Generation and Multimodal Intelligence System for Precision Clinical Nutrition and Metabolic Health Tracking" International Journal of Computer Science and Engineering Development, V1(4): Page(163-171) September - October 2026. ISSN: 3139-0862. www.ijcsed.com. Published by Scientific and Academic Research Publishing.