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Tunga Venkanna Babu is an Assistant Professor in the Department of Electronics and Communication Engineering at Hyderabad Institute of Technology and Management, Hyderabad, Telangana, India. His academic interests include electronics, communication engineering, and intelligent technological systems. He contributes to the application of Agentic AI in intelligent e-commerce and sales prediction.
warning mechanisms. It investigates disaster detection, the amalgamation of diverse environmental data, predictive analytics through machine learning and deep learning, spatiotemporal modelling, anomaly identification, uncertainty assessment, hazard-specific agents, coordination among multiple agents, edge and cloud computing intelligence, integration of satellite and remote sensing, geospatial analysis, and the dissemination of intelligent warnings. The section also explores elucidation, human-in-the-loop decision processes, cybersecurity, data management, communication dependability, and ethical implications. A conceptual framework is suggested wherein specialised agents perpetually observe threats, employ relevant analytical models, assess forecasts, and disseminate evidence-based alerts within established operational parameters. Future trajectories encompassing digital twins, multimodal foundational models, autonomous sensing, federated learning, and cooperative multi-agent disaster management systems are likewise examined. The section underscores that Agentic AI ought to enhance traditional scientific forecasting and emergency management frameworks instead of functioning as an unbridled autonomous entity.
A proactive e-commerce system may incessantly oversee transactions, customer engagements, inventory statuses, product trends, marketing initiatives, competitor data, and external influences. Cognitive agents are capable of predicting demand, identifying sales irregularities, recognising new products, examining client demographics, suggesting products, enhancing advertising tactics, facilitating dynamic pricing under established guidelines, and managing inventory restocking. This section offers an extensive structure for Agentic AI in forecasting sales and enhancing intelligent e-commerce. It examines sources of e-commerce data, sales forecasting, demand prediction, customer behaviour modelling, recommendation systems, inventory optimisation, dynamic pricing, promotion optimisation, customer segmentation, conversational commerce, sentiment analysis, fraud detection, supply chain coordination, edge-cloud architectures, multi-agent systems, explainability, uncertainty, privacy, cybersecurity, fairness, and human-in-the-loop governance. Mathematical models and assessment criteria are provided for prediction, suggestion, stock management, pricing strategies, and agent efficacy. The chapter wraps up by outlining prospective research avenues related to multimodal commerce agents, foundational models, autonomous merchandising, federated learning, digital twins, and tailored intelligent commerce ecosystems.