# Agentic AI Framework for Smart Inventory Replenishment

arXiv: https://arxiv.org/abs/2511.23366 · HTML: https://mlsanity.com/paper/2511.23366/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 28 Nov 2025.

## Builds on (most cited first, 5 of 5)

- [Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chains](https://mlsanity.com/paper/2006.04037/) (2020)
- [Agentic AI Framework for Individuals with Disabilities and Neurodivergence: A Multi-Agent System for Healthy Eating, Daily Routines, and Inclusive Well-Being](https://mlsanity.com/paper/2511.22737/) (2025)
- [Agentic AI Framework for Cloudburst Prediction and Coordinated Response](https://mlsanity.com/paper/2511.22767/) (2025)
- [Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management](https://mlsanity.com/paper/2303.10382/) (2023)
- [Blockchain-based Smart-IoT Trust Zone Measurement Architecture](https://mlsanity.com/paper/2001.03002/) (2020)

## Built on by (most cited first, 5 of 5)

- [Toward Trustworthy Agentic AI: A Multimodal Framework for Preventing Prompt Injection Attacks](https://mlsanity.com/paper/2512.23557/) (2025)
- [A Blockchain-Monitored Agentic AI Architecture for Trusted Perception-Reasoning-Action Pipelines](https://mlsanity.com/paper/2512.20985/) (2025)
- [Flowr -- Scaling Up Retail Supply Chain Operations Through Agentic AI in Large Scale Supermarket Chains](https://mlsanity.com/paper/2604.05987/) (2026)
- [XAI-SDN: An Explainable Entropy-Guided Machine Learning Framework for Real-Time DDoS Detection in Software Defined Networks](https://mlsanity.com/paper/2609.05701/) (2026)
- [A Hybrid Agentic AI Framework for Intelligent Supply Chain Analytics](https://mlsanity.com/paper/2609.13561/) (2026)

## Abstract (arXiv)

In contemporary retail, the variety of products available (e.g. clothing, groceries, cosmetics, frozen goods) make it difficult to predict the demand, prevent stockouts, and find high-potential products. We suggest an agentic AI model that will be used to monitor the inventory, initiate purchase attempts to the appropriate suppliers, and scan for trending or high-margin products to incorporate. The system applies demand forecasting, supplier selection optimization, multi-agent negotiation and continuous learning. We apply a prototype to a setting in the store of a middle scale mart, test its performance on three conventional and artificial data tables, and compare the results to the base heuristics. Our findings indicate that there is a decrease in stockouts, a reduction of inventory holding costs, and an improvement in product mix turnover. We address constraints, scalability as well as improvement prospect.