FinAgent: An Agentic AI Framework Integrating Personal Finance and Nutrition Planning

TL;DR

This work tackles the problem of concurrent budget management and nutritional planning under real-time price volatility. It introduces FinAgent, a price-aware agentic AI built as a multi-agent system that jointly optimizes budgets and diets via a linear program with a substitution graph, adapting to price shocks and individual health needs. The approach achieves 12-18% cost reductions and nutrient adequacy above 95% in synthetic simulations and a four-week Saudi household case, demonstrating robustness to price fluctuations up to 30%. The study highlights practical implications for affordable, culturally appropriate nutrition and aligns with SDG targets on Zero Hunger and Good Health, while outlining scalable deployment and governance considerations for real-world use.

Abstract

The issue of limited household budgets and nutritional demands continues to be a challenge especially in the middle-income environment where food prices fluctuate. This paper introduces a price aware agentic AI system, which combines personal finance management with diet optimization. With household income and fixed expenditures, medical and well-being status, as well as real-time food costs, the system creates nutritionally sufficient meals plans at comparatively reasonable prices that automatically adjust to market changes. The framework is implemented in a modular multi-agent architecture, which has specific agents (budgeting, nutrition, price monitoring, and health personalization). These agents share the knowledge base and use the substitution graph to ensure that the nutritional quality is maintained at a minimum cost. Simulations with a representative Saudi household case study show a steady 12-18\% reduction in costs relative to a static weekly menu, nutrient adequacy of over 95\% and high performance with price changes of 20-30%. The findings indicate that the framework can locally combine affordability with nutritional adequacy and provide a viable avenue of capacity-building towards sustainable and fair diet planning in line with Sustainable Development Goals on Zero Hunger and Good Health.

Figures (6)

• Figure 1: Architecture of the agentic AI framework integrating budgeting, nutrition, cultural preferences, and price awareness
• Figure 2: Multi-agent workflow illustrating inter-agent collaboration through the shared knowledge base
• Figure 3: End-to-end workflow from data collection to adaptive re-planning
• Figure 4: Nutritional adequacy across planning methods (mean over 30 runs).
• Figure 5: System adaptability under price shocks (SD over 30 runs).