Industry 4.0
Vol. 11 (2026), Issue 1, pg(s) 15-19

BUSINESS & “INDUSTRY 4.0”

A unified architectural framework for retail AI agents based on tiered functional decomposition

  • 1 Faculty of Mathematics and Informatics, Sofia University "St. Kliment Ohridski", Sofia - Bulgaria

Abstract

The rapid emergence of autonomous AI agents in commerce is reshaping how consumers interact with retailers, how decisions are made, and how value is exchanged across digital ecosystems. Yet despite accelerating innovation, the retail sector lacks a unified architectural model that defines how such agents should ingest information, reason about complex commercial contexts, and execute actions in a transparent, interoperable, and trustworthy manner. This paper introduces a unified architectural framework for retail AI agents based on tiered functional decomposition, offering a structured, three-tier model (Input, Model, Output) that standardizes agent behavior across diverse retail environments.
The Input Tier formalizes how agents acquire and normalize information, including user intent, preference profiles, product catalogs, pricing signals, inventory data, logistics constraints, and retailer policies. By defining a consistent intake layer, the framework ensures that agents operate on coherent, comparable, and semantically aligned data structures.
The Model Tier provides the cognitive core of the agent. It encompasses goal decomposition, relevance scoring, multi-objective optimization, negotiation logic, risk assessment, and alignment safeguards. This tier transforms raw inputs into structured decisions, enabling agents to act as rational, preference-aligned economic participants capable of navigating complex retail ecosystems.
The Output Tier defines how agents act on decisions and how they learn from outcomes. It includes action execution (e.g., purchasing, reserving, scheduling), transparent justification, user-agent dialogue, cross-agent communication, feedback ingestion, and adaptive preference updating. This tier closes the loop between perception, reasoning, and action, enabling continuous improvement and long-term alignment with user goals.
Together, these tiers form a modular, interoperable architecture that can be adopted by retailers, marketplaces, and technology providers to ensure predictable, explainable, and scalable agent behavior. The framework supports progressive capability levels, enabling retailers to evolve from informational agents to fully autonomous purchasing systems. By establishing a shared architectural foundation, this work aims to accelerate the development of agentic commerce and foster a more efficient, transparent, and user-aligned retail ecosystem.

Keywords

References

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