Retail Resilience Engine: An Agentic AI Framework for Building Reliable Retail Systems With Test-Driven Development Approach
Open accessAbstract
System reliability and operational resilience are two critical success factors in the retail industry that are directly connected to customer satisfaction and business sustainability. Staying competitive in today’s dynamic and rapidly evolving market requires rapid adaptability. However, it contradicts the reliability and resilience. This paper proposes an innovative solution, the Retail Resilience Engine (RRE), to establish a balance between these success factors and market demand. It is a unique framework that combines Test-Driven Development (TDD) with a Large Language Model (LLM). This framework follows the state-of-the-art Agentic-AI architecture. It effectively evaluates the decision-making process at rapid speed in retail by incorporating diverse factors, including inventory management, demand forecasting, and customer feedback. As a result, the system reliability is improved significantly. The experimental analysis of the proposed framework shows its decision-making is similar to human experts with a similarity index of 97.5%. It further proves the reliability of the system. The framework also scales effectively, maintaining high accuracy, precision, recall, and F1 scores across varying dataset sizes. The robustness analysis of the system demonstrates the agility enhancement across diverse retail domains, ensuring consistent performance with accuracy exceeding 90% across all tested scenarios. The integration of a creative filtering mechanism further enhances the performance of the RRE framework by preventing 98.2% of the irrelevant inputs. Overall, the proposed RRE framework demonstrates the impressive potential to transform retail systems by enhancing reliability, scalability, and decision-making quality through an Agentic-AI approach.
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Agentic AI exhibits self-defined and executed goal-oriented behavior.
“Self-defined and executed goal-oriented behavior”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [23]
Cites
Attention mechanisms in computer vision: A survey
Guo, M.-H., Xu, T.-X., Liu, J.-J., Liu, Z.-N., Jiang, P.-T., Mu, T.-J., Zhang, S.-H., Martin, R. R., Cheng, M.-M., & Hu, S.-M. (2022) · Computational Visual Media · doi:10.1007/s41095-022-0271-y · Cited by 2,566 · cited 1× · see citing works
“In the conceptual development of the transformer model, the relationship among token t i , mechanism computes query ( q i ), key ( k i ), and value ( v i ) vectors are defined by Equation 8 [45].”
Noise Reduction in Speech Processing
Cohen, I., Huang, Y. A., Chen, J., & Benesty, J. (2009) · Springer topics in signal processing · doi:10.1007/978-3-642-00296-0 · Cited by 1,466 · see citing works
A systematic review of systematic review process research in software engineering
Kitchenham, B., & Brereton, P. (2013) · Information and Software Technology · doi:10.1016/j.infsof.2013.07.010 · Cited by 1,094 · see citing works
AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications
Rashid, A. B., & Kausik, M. A. K. (2024) · Hybrid Advances · doi:10.1016/j.hybadv.2024.100277 · Cited by 764 · cited 1× · see citing works
“However, their ability to adapt to rapidly changing market dynamics remains limited [13].”
Yenduri, G., Ramalingam, M., Selvi, G. C., Supriya, Y., Srivastava, G., Maddikunta, P. K. R., Raj, G. D., Jhaveri, R. H., Prabadevi, B., Wang, W., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3389497 · Cited by 759 · see citing works
Attention mechanism in neural networks: where it comes and where it goes
Soydaner, D. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07366-3 · Cited by 441 · see citing works
How artificial intelligence will affect the future of retailing
Guha, A., Grewal, D., Kopalle, P. K., Haenlein, M., Schneider, M. J., Jung, H., Moustafa, R., Hegde, D. R., & Hawkins, G. (2021) · Journal of Retailing · doi:10.1016/j.jretai.2021.01.005 · Cited by 365 · see citing works
Understanding and Improving Layer NormalizationPreprint
Xu, J., Sun, X., Zhang, Z., Zhao, G., & Lin, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.07013 · Cited by 253 · see citing works
Pasupuleti, V., Thuraka, B., Kodete, C. S., & Malisetty, S. (2024) · Logistics · doi:10.3390/logistics8030073 · Cited by 248 · see citing works
Hsu, H., & Lachenbruch, P. A. (2014) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat05929 · Cited by 106 · see citing works
SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization
Faruqui, N., Yousuf, M. A., Whaiduzzaman, M., Azad, A., Alyami, S. A., Lió, P., Kabir, M. A., & Moni, M. A. (2023) · Electronics · doi:10.3390/electronics12173541 · Cited by 105 · see citing works
Theodorakopoulos, L., Theodoropoulou, A., & Stamatiou, Y. C. (2024) · Eng—Advances in Engineering · doi:10.3390/eng5030068 · Cited by 101 · see citing works
Retail Supply Chain Management
Agrawal, N., & Smith, S. A. (2015) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-1-4899-7562-1 · Cited by 96 · see citing works
Anica-Popa, I., Anica-Popa, L.-E., Radulescu, C., & Vrîncianu, M. (2021) · Amfiteatru Economic · doi:10.24818/ea/2021/56/120 · Cited by 91 · see citing works
Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework
Sathupadi, K., Achar, S., Bhaskaran, S. V., Faruqui, N., Abdullah-Al-Wadud, M., & Uddin, J. (2024) · Sensors · doi:10.3390/s24247918 · Cited by 89 · cited 1× · see citing works
“Integrating it with the Internet of Things (IoT) and edge computing devices will enable decentralized decision-making and reduce latency, which is critical for real-time retail operations [56].”
Deep-IDS: A Real-Time Intrusion Detector for IoT Nodes Using Deep Learning
Racherla, S., Sripathi, P., Faruqui, N., Kabir, M. A., Whaiduzzaman, M., & Shah, S. A. (2024) · IEEE Access · doi:10.1109/access.2024.3396461 · Cited by 89 · see citing works
PRISMA; Preferred Reporting Items for Systematic Reviews and Meta-Analyses
Assar, S., Jalalpour, S., Ayoubi, F. A., Rahmani, M. R., & Rezaeian, M. (2016) · Journal of Rafsanjan University of Medical Sciences · Cited by 73 · see citing works
Faruqui, N., Yousuf, M. A., Kateb, F., Hamid, M. A., & Monowar, M. M. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e21520 · Cited by 59 · see citing works
Artificial intelligence in retail – a systematic literature review
Heins, C. (2022) · foresight · doi:10.1108/fs-10-2021-0210 · Cited by 50 · see citing works
Vulnerability of the accessibility to grocery shopping in the event of flooding
Wiśniewski, S., Borowska-Stefańska, M., Kowalski, M., & Sapińska, P. (2020) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2020.102510 · Cited by 44 · see citing works
Madeyski, L. (2009) · Springer eBooks · doi:10.1007/978-3-642-04288-1 · Cited by 43 · see citing works
Test-Driven Development and LLM-based Code Generation
Mathews, N. S., & Nagappan, M. (2024) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3691620.3695527 · Cited by 42 · see citing works
Tokenization in the Theory of Knowledge
Friedman, R. (2023) · Encyclopedia · doi:10.3390/encyclopedia3010024 · Cited by 41 · see citing works
A Novel Front Door Security (FDS) Algorithm Using GoogleNet-BiLSTM Hybridization
Paula, L. P. O., Faruqui, N., Mahmud, I., Whaiduzzaman, M., Hawkinson, E., & Trivedi, S. (2023) · IEEE Access · doi:10.1109/access.2023.3248509 · Cited by 39 · see citing works
Klumpp, M., & Loske, D. (2021) · Sustainability · doi:10.3390/su13105650 · Cited by 36 · see citing works
Kformer: Knowledge Injection in Transformer Feed-Forward Layers
Yao, Y., Huang, S., Li, D., Wei, F., Chen, H., & Zhang, N. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17120-8_11 · Cited by 32 · see citing works
Building sustainable resilient supply chain in retail sector under disruption
Ekinci, E., Sezer, M. D., Mangla, S. K., & Kazançoğlu, Y. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.139980 · Cited by 27 · see citing works
Saliency-based fabric defect detection via bag-of-words model
Kanwal, M., Riaz, M. M., Ali, S. S., & Ghafoor, A. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-022-02379-w · Cited by 21 · see citing works
AI-Analyst: An AI-Assisted SDLC Analysis Framework for Business Cost Optimization
Faruqui, N., Thatoi, P., Choudhary, R., Rončević, I., Alqahtani, H., Sarker, I. H., & Khanam, S. (2024) · IEEE Access · doi:10.1109/access.2024.3519423 · Cited by 21 · see citing works
Factors Affecting the Adoption of Emerging Technologies in Last-Mile Delivery in the Retail Industry
Ismail, R.-E., & Jokonya, O. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.511 · Cited by 17 · see citing works
Fusing color, depth and histogram maps for saliency detection
Kanwal, M., Riaz, M. M., Ali, S. S., & Ghafoor, A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12165-y · Cited by 17 · see citing works
DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media
Hossain, M. E., Faruqui, N., Mahmud, I., Jan, T., Whaiduzzaman, M., & Barros, A. (2023) · Applied Sciences · doi:10.3390/app132212300 · Cited by 16 · see citing works
Image attention retargeting using defocus map and bilateral filter
Kanwal, M., Riaz, M. M., Ali, S. S., & Ghafoor, A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08813-w · Cited by 16 · see citing works
Test-Driven Development (TDD) in Small Software Development Teams: Advantages and Challenges
Ramzan, H. A., Ramzan, S., & Kalsum, T. (2024) · International Conference on Advancements in Computational Sciences (ICACS) · doi:10.1109/icacs60934.2024.10473291 · Cited by 12 · see citing works
Abideen, A. Z., Pyeman, J., Mutalib, S., Kamaruddin, K. A., & Rahman, N. A. (2024) · International Journal of Industrial Engineering and Operations Management · doi:10.1108/ijieom-07-2024-0040 · Cited by 12 · see citing works
Unveiling underwater structures: pyramid saliency detection via homomorphic filtering
Kanwal, M., Riaz, M. M., & Ghafoor, A. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18474-8 · Cited by 11 · see citing works
Cloud IaaS Optimization Using Machine Vision at the IoT Edge and the Grid Sensing Algorithm
Faruqui, N., Achar, S., Racherla, S., Dhanawat, V., Sripathi, P., Islam, M. M., Uddin, J., Othman, M. A., Samad, M. A., & Choi, K. (2024) · Sensors · doi:10.3390/s24216895 · Cited by 11 · see citing works
Assessment of Deep Neural Network and Gradient Boosting Machines for Credit Risk Prediction Accuracy
Sakri, S. B. (2022) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn56167.2022.10008264 · Cited by 9 · see citing works
Exploring the Connection between the TDD Practice and Test Smells—A Systematic Literature Review
Marabesi, M., García‐Holgado, A., & García‐Peñalvo, F. J. (2024) · Computers · doi:10.3390/computers13030079 · Cited by 9 · see citing works
Empirical study on quality management for product-service systems in industrial environment
Pasch, F., Rybski, C., & Jochem, R. (2016) · Business Process Management Journal · doi:10.1108/bpmj-01-2016-0023 · Cited by 9 · see citing works
Zhao, T., & Faruqui, N. (2024) · IEEE Access · doi:10.1109/access.2024.3512594 · Cited by 7 · see citing works
Rahman, A., Cysneiros, L. M., & Berry, D. M. (2024) · ACM Symposium on Applied Computing · doi:10.1145/3605098.3635901 · Cited by 6 · see citing works
Testability-driven development: An improvement to the TDD efficiency
Parsa, S. A., Nasrabadi, M. Z., & Turhan, B. (2024) · Computer Standards & Interfaces · doi:10.1016/j.csi.2024.103877 · Cited by 5 · see citing works
Experiences and advances in reliability in retail projects
Koller, M. J. D., Carranza, M. E., Martinez, C., & Guzman, M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13242-1.00027-8 · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 45 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Bandi, A., Kongari, B., Naguru, R., Pasnoor, S., & Vilipala, S. V. (2025) · Future Internet · doi:10.3390/fi17090404 · Cited by 207 · see citing works
Brohi, S., Mastoi, Q.-U.-A., Jhanjhi, N. Z., & Pillai, T. R. (2025) · Algorithms · doi:10.3390/a18080499 · Cited by 69 · see citing works
The Rise of Agentic AI: Synthesis of Current Knowledge and Future Research Agenda
Islam, M. A., Somu, S., & Aldaihani, F. M. F. (2025) · Global Business and Organizational Excellence · doi:10.1002/joe.70019 · Cited by 33 · see citing works
A systematic analysis of generative artificial intelligence for supply chain transformation
Bahroun, Z., Saihi, A., As’ad, R. A., & Tanash, M. (2025) · Supply Chain Analytics · doi:10.1016/j.sca.2025.100188 · Cited by 25 · see citing works
Agentic AI Systems: Architecture and Evaluation Using a Frictionless Parking Scenario
Khamis, A. M. (2025) · IEEE Access · doi:10.1109/access.2025.3590264 · Cited by 20 · see citing works
Song, Z., Xie, Y., Yang, L., & Zhao, Y. (2026) · International Journal of Production Research · doi:10.1080/00207543.2026.2641103 · Cited by 20 · see citing works
Customer Experience in E-Commerce: A Systematic Review of Metrics, Models, and the Role of AI
Mittameedi, S. K., Dogra, V., & Sayal, A. (2025) · IEEE Access · doi:10.1109/access.2025.3600729 · Cited by 17 · see citing works
Amalfitano, D., Metzger, A., Autili, M., Fulcini, T., Hey, T., Keim, J., Pelliccione, P., Scotti, V., Koziolek, A., Mirandola, R., et al. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3788879 · Cited by 17 · see citing works
Arya, V. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104693 · Cited by 16 · see citing works
Mishra, L. N., & Senapati, B. (2026) · IEEE Access · doi:10.1109/access.2026.3655398 · Cited by 9 · see citing works
Self-citationWu, L., Lu, W., Zou, Y., An, H., & Wang, B. (2026) · International Journal of Logistics Research and Applications · doi:10.1080/13675567.2026.2636594 · Cited by 9 · see citing works
Senapati, B., Panyaram, S., Jaiswal, C., Anand, M. R., Davaluri, S. K., Challagulla, V., & Pradhan, S. (2025) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit64391.2025.11103699 · Cited by 4 · see citing works
Self-citationTiwari, N. (2025) · International Conference on Artificial Intelligence and Machine Vision (AIMV) · doi:10.1109/aimv66517.2025.11203535 · Cited by 4 · see citing works
Agentic AI: a review, applications and open research challenges
Khalid, O., Farooqi, A. U. H., & Bilal, M. (2026) · Computing · doi:10.1007/s00607-026-01676-3 · Cited by 4 · see citing works
Generative AI and management analytics: survey
Li, J., Fu, L., & Wang, Y. F. (2026) · Journal of Management Analytics · doi:10.1080/23270012.2026.2652312 · Cited by 3 · see citing works
Empowering Text Classification with Agentic AI: A Systematic Review
Sharma, V., Limbachiya, Y., & Oza, D. (2025) · International Conference on Artificial Intelligence and Machine Vision (AIMV) · doi:10.1109/aimv66517.2025.11203695 · Cited by 2 · see citing works
Agentic AI-Driven Optimizing Demand Forecasting in Retail Systems with AI-Based Predictive Analytics
Tiwari, N., & Prasad, S. K. (2025) · International Conference on Artificial Intelligence and Machine Vision (AIMV) · doi:10.1109/aimv66517.2025.11203602 · Cited by 2 · see citing works
BPMN DMN decision table generation based on agentic AI for critical applications
Meddeb, S., Batti, S., & Fathallah, H. (2026) · Business Process Management Journal · doi:10.1108/bpmj-08-2025-1254 · Cited by 2 · see citing works
Agentic AI for Conversational Flight Reservation Systems
Rishwanth, S., Yashwanth, L., & Shiny, R. M. (2025) · International Conference on Mobile Networks and Wireless Communications (ICMNWC) · doi:10.1109/icmnwc66779.2025.11354415 · Cited by 1 · see citing works
An Overview of Agentic AI: Perspective and Design Flows
Kumar, D., Chaudhri, S. N., Kumar, M. K., Kumari, S., Agarwal, S., & Chauhan, N. K. (2026) · International Conference on Emerging Systems and Intelligent Computing (ESIC) · doi:10.1109/esic68176.2026.11496280 · Cited by 1 · see citing works
Mishra, M. R., & Ansari, D. N. (2026) · International Journal of Artificial Intelligence and Machine Learning · doi:10.51483/ijaiml.6.2s.2026.23-45 · Cited by 1 · see citing works
An Agentic AI and LLM-Based Framework for Probabilistic Cost Estimation from Fragmented BIM Data
Wu, L., Zhang, Q., Xu, R., Zhang, Y., Ghansah, F. A., & Chen, X. (2026) · Intelligent infrastructure and construction · doi:10.3390/iic2030008 · Cited by 1 · see citing works
Márquez, A. C., & Fernández, J. F. G. (2026) · Computers & Industrial Engineering · doi:10.1016/j.cie.2026.112285 · Cited by 1 · see citing works
Kavitha, V., Suresh, K. T., Vishnu, P. R., & Kumaresan, T. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3246-8.ch010 · Cited by 1 · see citing works
David, S., K, S. S., & J, S. S. (2025) · International Conference on Smart Structures and Systems (ICSSS) · doi:10.1109/icsss66939.2025.11346248
Agentic Artificial Intelligence: Transforming the Future of Retail
Zaidan, A. S., Almanwari, H. S. A., Alshammri, W. S., Khaw, K. W., & Arianpoor, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-15806-2_35
The Power of Agentic Artificial Intelligence in Modern Product Recommendations and Advertising
Mullah, N. S., Zainon, W. M. N. W., & Sinawi, S. A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-15806-2_2
Categorical Boosting Enhanced AI-Powered Demand Forecasting in Retail Environment
Marappan, K., P.Sathiyaseelan, Muthulekshmi, M., Shofia Priya Dharshini, D., Bhanumathi, M., & Adikesavaperumal (2025) · International Conference on Emerging Trends in Electronic Devices and Computational Techniques (EDCT) · doi:10.1109/edct65302.2025.11496048
Alcantara, J. A. C., & Atuncar, G. S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-22091-2_26
Jingar, N. K. (2026) · IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) · doi:10.1109/ic_aset69920.2026.11502212
Agentic AI Frameworks: Orchestration Architectures and Autonomous LLM-Based Reasoning Systems
Yashwanth, L., Rishwanth, S., & Shiny, R. M. (2026) · International Conference on Integrated Intelligence and Communication Systems (ICIICS) · doi:10.1109/iciics67880.2026.11483602
Supply Chain and Retail Demand Optimization Using Gen AI
Shreyas, K. C., & Naik, H. (2026) · International Conference on Emerging Trends in Mobile Computing and Sustainable Informatics (ICEMCSI) · doi:10.1109/icemcsi67638.2026.11602824
An Agentic AI-Based Prototype to Support Teaching-Learning Processes
Flores-Serrano, D., & Chicaiza, J. A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-32112-1_12
Impact of AI Agents on the Digital Supply Chain
Popova, P. E., & Popov, V. D. (2026) · International Conference on Electronics, Computers and Artificial Intelligence (ECAI) · doi:10.1109/ecai69016.2026.11613583
Janjani, D. J., & Motwani, D. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-27163-1_6
Wu, L., Geng, L., Xue, J., Qian, Y., Liu, Z., & Wuni, I. Y. (2026) · AI in Civil Engineering · doi:10.1007/s43503-026-00107-8
Policy-Adherent Procurement Using a Multi-Agent AI Framework
Prasad, J., Issac, K. A., Simon, R., Sanjay, S., S, G. K., & Mohan, J. (2026) · International Conference on Smart Communication and Sustainable Technologies (ICSCST) · doi:10.1109/icscst70031.2026.11709524
- Authors
- Mishra, L. N., & Senapati, B.
- Affiliations
- Lowe's (United States) (US)
- University of Arkansas at Little Rock (US)
- Venue
- IEEE Access
- Published
- 2025
- Volume
- 13
- Pages
- 50226-50243
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4408564575
- Cited by
- 35
- References
- 45
- Citations by year
- 2026: 20 · 2025: 15
- FWCI
- 27.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Keywords
- agentic AI, test-driven development, large language models, retail decision-making, system reliability, operational resilience, inventory management, demand forecasting, customer feedback, input filtering, scalability
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