AGENTIC ARTIFICIAL INTELLIGENCE AS A TOOL FOR RESTRUCTURING MARKETING TECHNICAL DEBT IN THE CONTEXT OF HYPER-PERSONALIZATION
Abstract
The article provides a comprehensive study on the issue of Marketing Technical Debt (MTD) as a primary systemic barrier to implementing hyperpersonalization strategies in modern digital marketing. The relevance of the topic is driven by the fundamental shift in the marketing paradigm from static segmentation to dynamic customer experience management in real-time, requiring high technological adaptability from enterprises. The aim of the work is to theoretically substantiate and develop a conceptual model for using Agentic AI and Multi-Agent Systems (MAS) architecture as a strategic tool for restructuring MTD. The paper conducts a critical comparative analysis of Generative AI (GenAI) and Agentic AI. It is proven that GenAI, being a reactive technology, does not solve the architectural problems of «organizational drag», whereas agentic systems, due to their autonomy, proactivity, and planning capabilities, can automate complex workflows. The scientific novelty of the research lies in the development of an architectural model of a Multi-Agent System (MAS) for marketing, based on the principles of «cognitive modularity». The proposed model includes specialized agents (Data Collection, Decision-Making, Execution) united by a central Orchestrator, allowing for the encapsulation of legacy system complexity without the need for a complete infrastructure replacement («big bang» migration). The mechanism of «Integration as Intent» is described for the first time, demonstrating the ability of agents to autonomously create API connectors by analyzing technical documentation in real-time (illustrated by the Membrane Agent case). The practical value of the work lies in determining the economic effects of implementing agentic systems, specifically reducing Time-to-Market, lowering Customer Acquisition Costs (CAC) by up to 50%, and increasing conversion rates. The transition to the «Agentic Commerce» model is outlined, where interaction occurs in an Agent-to-Agent (A2A) format, and the introduction of Audit Agents is proposed to mitigate ethical risks, such as algorithmic bias and «digital heroin.» It is concluded that the integration of Agentic AI is a necessary condition for transforming technical debt from an operational burden into a managed asset.
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