InventoryGPT: An LLM-Powered Agentic Framework for Real-Time Retail Inventory Optimization/Management

InventoryGPT: An LLM-Powered Agentic Framework for Real-Time Retail Inventory Optimization/Management Prithvi Vasireddy College of Engineering Northeastern University Boston, MA Abstract—Traditional inventory management systems, characterized by architectural rigidity and reliance on historical data, are increasingly ill-equipped to handle the volatility and complexity of the modern retail landscape. This systemic inadequacy leads to significant financial losses from persistent issues such as stockouts and overstocking, which collectively cost the global retail industry nearly $1.8 trillion annually. To address these challenges, this paper introduces Inventory GPT, a novel agentic framework designed for real-time, intelligent inventory optimization. Built on the Google Cloud Platform (GCP), the system integrates a real-time data pipeline leveraging Cloud Pub/Sub, Dataflow, and BigQuery with a generative intelligence layer powered by a Large Language Model (LLM) hosted on Vertex AI. The core innovation of the framework is a Retrieval-Augmented Generation (RAG) mechanism that grounds the LLM in private, real-time enterprise data, enabling it to perform complex reasoning and decision-making tasks with high factual accuracy. Inventory GPT’s primary contributions include enabling dynamic, multi-factor demand forecasting, facilitating proactive stock level optimization, and democratizing data access through an intuitive natural language interface. By transforming inventory management from a reactive, manual process into a proactive, automated one, this framework offers a pathway to enhanced operational agility, reduced costs, and improved profitability for general retail enterprises. Index Terms—Inventory Management, Large Language Models, Generative AI, Google Cloud Platform, Retail Technology, Demand Forecasting, Supply Chain Optimization, RetrievalAugmented Generation. I. INTRODUCTION A. The Strategic Imperative of Inventory Management Inventory management serves as the operational backbone of the retail sector, directly influencing profitability, cash flow, and customer satisfaction [51]. The ability to maintain optimal stock levels—having the right product, in the right place, at the right time—is a critical determinant of success. However, achieving this balance is a persistent challenge, and the financial consequences of failure are staggering. Globally, retailers lose an estimated $1.8 trillion each year due to inventory mismanagement, a figure that encompasses both the lost revenue from stockouts and the capital erosion from overstocking [43]. These losses underscore the strategic imperative for robust, intelligent inventory control systems. B. The Vicious Cycle of Legacy Systems Many retail organizations remain encumbered by legacy systems that perpetuate a vicious cycle of inefficiency. This cycle often begins with inaccurate demand forecasting, a fundamental flaw that triggers a cascade of operational problems [23], [25]. Misjudging consumer demand inevitably leads to one of two costly outcomes: stockouts, which result in immediate lost sales and long-term damage to customer loyalty as shoppers turn to competitors, or overstocking, which ties up working capital, inflates carrying costs for storage and insurance, and increases the risk of inventory obsolescence [25], [48], [55]. These inventory imbalances are further exacerbated by deep-seated operational inefficiencies, including a continued reliance on error-prone manual processes, inconsistent tracking across disparate systems, and disorganized warehouse logistics [25], [33]. The result is a reactive operational posture where managers are constantly fighting fires rather than executing strategic plans. C. The Paradigm Shift to Agentic AI The recent advent of Large Language Model (LLM)-based agentic frameworks represents a fundamental paradigm shift in addressing these challenges. Unlike traditional software, which operates based on predefined rules and static algorithms, agentic AI systems can interpret complex goals, decompose them into manageable sub-tasks, select and use appropriate tools, and reason over vast amounts of information to orchestrate multi-step workflows [19], [35]. This capability signals a move away from passive data reporting, characteristic of conventional Business Intelligence (BI) tools, toward a model of proactive, intelligent automation where the system acts as a cognitive partner in the decision-making process. The analysis of persistent inventory challenges reveals a systemic dysfunction; issues such as stockouts are not isolated failures but rather symptoms of a deeper architectural mismatch between static legacy systems and dynamic market realities. The root cause is the static, siloed nature of technologies like traditional Enterprise Resource Planning (ERP) systems, which cannot cope with the interconnected, real-time demands of modern retail supply chains. This justifies the need for a completely new, integrated architecture rather than incremental upgrades. D. Introducing Inventory GPT This paper proposes Inventory GPT, an embodiment of this new paradigm. It is an end-to-end intelligent framework designed to provide real-time visibility, predictive insights, and interactive control over the entire inventory lifecycle. By leveraging a powerful LLM grounded in real-time enterprise data, Inventory GPT aims to break the vicious cycle of legacy systems and empower retailers with the agility to thrive in a dynamic market. Table I provides a comparative overview of the traditional approach versus the proposed LLM-agentic framework. E. Paper Organization The remainder of this paper is structured as follows. Section II details the specific limitations of conventional inventory management systems. Section III presents the comprehensive system architecture of Inventory GPT on the Google Cloud Platform. Section IV provides a detailed guide to the implementation of the framework. Section V explores the core capabilities and applications of the system. Section VI discusses anticipated impacts, implementation challenges, and future research directions. Finally, Section VII concludes with a summary of the paper’s contributions. II. LIMITATIONS OF CONVENTIONAL INVENTORY MANAGEMENT SYSTEMS The operational deficiencies in retail inventory management are not merely procedural; they are deeply rooted in the technological limitations of the conventional systems that underpin them. These systems, often centered around legacy ERP platforms, suffer from architectural rigidity, process inefficiencies, and outdated analytical methods. A. Architectural Rigidity and Data Fragmentation Traditional ERP systems were typically designed as monolithic, on-premise solutions for a more stable and predictable business environment [13], [40]. This architectural paradigm is fundamentally misaligned with the needs of modern, omnichannel retail. A primary consequence of this design is the creation of pervasive data silos. Critical information related to inventory is often fragmented across disparate modules, such as a Warehouse Management System (WMS), a Transportation Management System (TMS), and various order management systems, with little to no seamless data exchange between them [13], [40]. This fragmentation makes it impossible to achieve a single, unified view of inventory across the enterprise, hindering accurate and timely decision-making [11], [22], [23], [33]. Furthermore, the lack of standardized APIs in these older systems makes integration with modern, cloud-native tools and data sources a complex and costly endeavor, effectively isolating the core inventory system from valuable external data streams [10], [13]. B. Inefficacy of Manual and Spreadsheet-Based Processes Despite the availability of digital tools, a surprising number of retail operations continue to rely on manual and spreadsheet-based processes for critical inventory tasks [33]. Manual inventory counts, data entry into spreadsheets, and email-based order tracking are common practices that are inherently slow, susceptible to human error, and completely unscalable [25], [32], [48]. These methods introduce significant data latency, meaning the inventory data in the system of record can be hours or even days out of date. This lag creates critical discrepancies between recorded stock levels and the physical reality in the warehouse or on the store shelf, leading to situations where items marked ”in stock” online are unavailable for fulfillment [10], [11]. The combination of data silos, manual entry, and inconsistent tracking methods creates a form of ”data debt.” The data is not only inaccurate in the present, but its historical record is also corrupted, making future analysis unreliable. This debt compounds over time, eroding trust in the data and forcing managers to rely on intuition rather than evidence. C. Deficiencies in Traditional Forecasting Models Conventional demand forecasting models form the analytical core of most legacy inventory systems, yet they suffer from critical deficiencies. The most significant of these is an overreliance on historical sales data [31]. While historical data is valuable, using it in isolation renders the models incapable of adapting to dynamic market conditions, such as sudden shifts in consumer trends, the launch of new products with no sales history, or unforeseen external disruptions like supply chain delays [17], [43]. These models are often blind to a host of crucial external variables that influence demand, including competitor pricing and promotions, macroeconomic indicators, marketing campaigns, and even local weather patterns [31], [47]. This narrow focus on past performance in a stable environment leads to chronically inaccurate forecasts in the volatile reality of modern retail, directly causing the costly stock imbalances that plague the industry. III. THE INVENTORY GPT SYSTEM ARCHITECTURE The Inventory GPT framework is architected as a cohesive, end-to-end system on the Google Cloud Platform, designed to overcome the limitations of legacy systems by establishing a real-time, intelligent, and interactive operational core. The architecture is composed of four distinct but interconnected layers: a data ingestion and processing layer, a knowledge corpus and retrieval layer, a generative intelligence layer, and an agentic action and interface layer. A high-level diagram of the architecture is presented in Fig. 1. A. Real-Time Data Ingestion and Processing Layer This foundational layer is responsible for creating a canonical, real-time ”single source of truth” for all inventory-related data. It is designed to capture and process high-velocity data streams from a multitude of sources. TABLE I COMPARISON OF TRADITIONAL VS. LLM-AGENTIC INVENTORY MANAGEMENT Attribute Traditional Inventory Management Inventory GPT Framework Data Model Periodic, Batched Updates Real-Time, Event-Driven Streaming Forecasting Statistical, Historical-Based Dynamic, Multi-Factor AI-Driven Data Access Siloed, Expert-Only (SQL/BI Tools) Unified, Democratized (Natural Language) User Interface Complex, Rigid Forms & Reports Conversational, Interactive Paradigm Reactive (Responding to Past Events) Proactive, Predictive (Anticipating Future Needs) Fig. 1. A conceptual diagram illustrating the flow of data from real-time sources through the GCP processing pipeline, into the RAG knowledge corpus, and finally to the LLM agent for reasoning and user interaction. •Google Cloud Pub/Sub: Transactional data from Pointof-Sale (POS) systems, e-commerce platforms, warehouse management systems (WMS), and IoT-enabled scanners is ingested as a stream of events. Cloud Pub/Sub serves as a highly scalable, durable, and asynchronous messaging service that decouples data producers from consumers, ensuring the system can handle massive and fluctuating event volumes without data loss [57]. •Google Cloud Dataflow: As events flow from Pub/Sub, Dataflow provides a serverless, unified stream and batch data processing service. It is used to execute data transformation pipelines that cleanse, enrich, and structure the raw event data. For example, a Dataflow job might join POS transaction data with product master data, calculate real-time sales velocity, and format the output for analytical consumption [42]. •Google Cloud BigQuery: The transformed, analysisready data is streamed directly into BigQuery, a serverless, petabyte-scale data warehouse. BigQuery’s architecture is optimized for large-scale, real-time analytics, allowing for complex SQL queries over massive datasets to be executed in seconds. This capability is critical for the ”live” nature of Inventory GPT, providing the upto-the-second data needed for accurate decision-making [28], [57], [59]. B. Knowledge Corpus and Retrieval Layer (RAG) This layer functions as the LLM’s dynamic, long-term memory, grounding its reasoning in the specific, private, and timely operational context of the retail enterprise. It is built upon the Retrieval-Augmented Generation (RAG) framework. •Data Vectorization: Structured data from BigQuery (e.g., current stock levels per SKU, supplier lead times, sales trends) and unstructured data (e.g., PDF marketing plans, supply chain disruption alerts, competitor press releases) are periodically processed. The Vertex AI Embedding API is used to convert this diverse data into high-dimensional numerical vectors, or embeddings, that capture semantic meaning [27]. •Vector Indexing and Retrieval: These embeddings are stored and indexed in Vertex AI Vector Search. This is a high-performance, managed vector database designed for efficient similarity searching [44]. When a user poses a query to the system, the RAG orchestrator first converts the query into an embedding and then uses Vector Search to retrieve the most semantically relevant data chunks from the indexed knowledge corpus. This retrieved information—be it a set of real-time inventory figures or a paragraph from a marketing brief—provides the necessary factual context for the LLM [21], [24], [27]. C. Generative Intelligence and Reasoning Layer This layer serves as the cognitive core of the Inventory GPT system, where raw data and retrieved context are synthesized into intelligent insights and actionable plans. •LLM Hosting and Management: The system utilizes a powerful, state-of-the-art foundation model, such as Google’s Gemini, which is hosted, managed, and finetuned using Vertex AI Model Garden and the broader Vertex AI platform [7], [8], [16]. Vertex AI provides a comprehensive MLOps environment that handles the complexities of model deployment, autoscaling, monitoring, and security, allowing the model to operate securely within the retailer’s dedicated GCP project and VPC network [3], [38]. •Contextual Reasoning: This layer receives two key inputs: the original user query and the rich, factual context provided by the RAG layer. The LLM’s function is not merely to generate a plausible-sounding text but to perform complex reasoning over this combined information. It can identify patterns, infer relationships, evaluate tradeoffs, and formulate a coherent, multi-step plan to address the user’s request in its entirety [19], [35]. D. Agentic Action and Interface Layer This is the execution layer, where the LLM’s reasoned plan is translated into concrete actions and communicated back to the user through a natural language interface. •Natural Language Understanding (NLU): The process begins with the agent interpreting the user’s intent from a conversational query (e.g., ”What’s driving the high rate of return for our new footwear line?”) [58]. •Tool Use and Orchestration: Based on the inferred intent, the agent autonomously selects and orchestrates the use of various ”tools” to gather the necessary information. A primary tool is a Natural Language-to-SQL generator, which translates the user’s question into a precise, executable BigQuery SQL query to retrieve structured data [5], [26]. Other tools could include invoking external APIs (e.g., for shipping carrier tracking) or executing a Python script for advanced statistical analysis. •Natural Language Generation (NLG): Once the tools have been executed and their outputs (e.g., SQL query results, API responses) are returned, the agent synthesizes this structured and unstructured information. It then generates a comprehensive, human-readable response that not only answers the initial question but also provides explanatory context, highlights key insights, and may even suggest actionable next steps [58]. The RAG architecture is the critical component that makes an LLM viable for enterprise use, as it directly solves two fundamental problems: the LLM’s lack of access to real-time, private data and its inherent propensity to ”hallucinate” or fabricate information. An off-the-shelf LLM has no knowledge of a specific retailer’s inventory [24]. The RAG pipeline addresses this by first retrieving the actual data from the system of record (BigQuery via Vector Search). This ”grounds” the LLM’s response in factual, timely data, drastically reducing hallucinations and making the system reliable. By continuously indexing the real-time data stream, the RAG system ensures the LLM’s knowledge is never stale, effectively paying off the ”data debt” of legacy systems with every query. IV. IMPLEMENTATION DETAILS Implementing the Inventory GPT framework involves a phased approach that leverages a suite of managed services on Google Cloud Platform. This section outlines the key steps required to build and deploy the system, from establishing the cloud foundation to deploying the intelligent agent. A. Phase 1: GCP Project and API Setup The initial phase involves preparing the GCP environment. 1) Project Creation: A new GCP project is created to serve as a dedicated container for all resources, billing, and permissions related to Inventory GPT. This can be done via the Google Cloud Console or the gcloud command-line tool [9]. 2) API Enablement: Several core APIs must be enabled for the project to allow the services to interact. These include: •Vertex AI API (aiplatform.googleapis.com) •BigQuery API (bigquery.googleapis.com) •Pub/Sub API (pubsub.googleapis.com) •Cloud Dataflow API (dataflow.googleapis.com) •Cloud Storage API (storage.googleapis.com) 3) IAM Configuration: A service account is created with specific Identity and Access Management (IAM) roles to ensure secure, programmatic access for the different components. Essential roles include Vertex AI User,BigQuery Data Editor,Pub/Sub Publisher/Subscriber, and Dataflow Worker [1], [14]. This follows the principle of least privilege, granting only the necessary permissions for each service to perform its function. B. Phase 2: Real-Time Data Pipeline Construction This phase focuses on building the data backbone of the system. 1) Pub/Sub Topic Creation: A Pub/Sub topic is created to act as the central ingestion point for all real-time inventory events. POS systems, e-commerce backends, and WMS are configured to publish messages (e.g., in JSON format) to this topic for every sale, return, or stock movement. 2) BigQuery Schema Definition: A destination table is created in BigQuery to store the processed inventory data. The schema is designed to capture key attributes such as SKU,product_name, location_id,transaction_type (e.g., SALE, RECEIPT), quantity,price, and a high-precision timestamp. 3) Dataflow Streaming Job: A streaming pipeline is developed using the Apache Beam SDK (typically in Python or Java) and deployed as a Dataflow job. This serverless job performs the following actions in real-time: •Reads raw event messages from the Pub/Sub topic. •Parses and validates the JSON payload. •Enriches the data by, for example, joining it with a product dimension table to add category information. •Writes the clean, structured records into the BigQuery table using the Storage Write API for lowlatency ingestion [1]. C. Phase 3: RAG System Configuration This phase builds the knowledge corpus that grounds the LLM. TABLE II INVENTORY GPT ARCHITECTURAL COMPONENTS ON GCP Architectural Layer GCP Service Function in Inventory GPT Key Benefit Data Ingestion Cloud Pub/Sub Ingests high-throughput event streams from POS, e-commerce, and WMS. Decouples data sources from processing, ensuring scalability and resilience. Data Processing Cloud Dataflow Cleanses, transforms, and enriches streaming data in real-time. Provides serverless, scalable ETL for analysis-ready data preparation. Data Warehousing BigQuery Stores all processed inventory data for real-time analytics and querying. Enables ultra-fast SQL queries on massive datasets; serves as the ”single source of truth.” Knowledge Corpus Vertex AI Vector Search Indexes and stores vector embeddings of structured and unstructured data. Facilitates rapid, semantic retrieval of relevant context for the LLM. Intelligence/Reasoning Vertex AI (Gemini Model) Interprets user queries, reasons over retrieved context, and formulates plans. Provides the core cognitive capabilities for advanced analysis and decisionmaking. Interface/Action Natural Language API / Custom Agents Translates natural language to SQL/API calls and synthesizes results into reports. Democratizes data access and automates complex analytical workflows. 1) Vector Search Setup: An index is created in Vertex AI Vector Search. Key configuration parameters include setting the IndexUpdateMethod to STREAM_UPDATE to allow for near real-time additions and specifying a distance metric like COSINE_DISTANCE for semantic similarity searches [4], [6]. 2) Index Endpoint Deployment: The newly created index is deployed to a public index endpoint. This process can take up to 30 minutes for the initial deployment as GCP provisions the underlying infrastructure but is much faster for subsequent updates [4], [6]. 3) RAG Corpus Creation: Using the Vertex AI SDK, a RAG Corpus is created. This acts as a logical container for the indexed data. The corpus is configured to use the previously created Vector Search index and endpoint as its backend vector store [6], [15]. 4) Data Ingestion and Vectorization: A recurring process (e.g., a Cloud Function or scheduled job) is established to populate the RAG Corpus: •It queries BigQuery for the latest inventory data and other structured information (e.g., supplier lead times). •It accesses unstructured documents (e.g., marketing plans, supply chain reports) from a Cloud Storage bucket. •It uses the Vertex AI Embedding API to convert these text chunks into numerical vectors. •These vectors, along with their source metadata, are then upserted into the RAG Corpus, making the information retrievable [4]. D. Phase 4: LLM Agent Deployment and Tooling This is where the ”intelligence” of the system is assembled. 1) Model Selection and Deployment: A suitable foundation model, such as Gemini 1.5 Pro, is selected from the Vertex AI Model Garden [8], [16]. For full control and security, the model is deployed to a dedicated Vertex AI Endpoint within the project’s VPC network. This ensures that inference requests and data remain within the private cloud environment [3], [38]. 2) Agent Logic Development: The core agent logic is developed, often using a framework like LangChain to simplify interactions with the LLM and tools [7]. The agent is designed to follow a Reason-and-Act (ReAct) loop: •It receives a user’s natural language query. •It uses the LLM to reason about the query and determine which tool to use next (e.g., the RAG retriever or the NL-to-SQL tool). •It executes the chosen tool. •It observes the tool’s output and repeats the process until it has enough information to answer the user’s query. 3) NL-to-SQL Tool Implementation: A key tool for the agent is the ability to query the structured data in BigQuery. This is implemented by providing the LLM with the BigQuery table schema and a few examples (fewshot prompting). When the agent needs structured data, it passes the user’s query and the schema information to the LLM, instructing it to generate a valid BigQuery SQL query. The agent then executes this query via the BigQuery API and uses the returned data in its reasoning process [5], [9]. V. CORE CAPABILITIES AND APPLICATIONS The synergistic integration of a real-time data foundation with an agentic LLM unlocks a suite of transformative capabilities that directly address the core deficiencies of traditional inventory management. Inventory GPT moves beyond static reporting to offer dynamic forecasting, proactive optimization, and democratized analytics. A. Dynamic and Context-Aware Demand Forecasting Inventory GPT fundamentally redefines demand forecasting by transforming it from a static, algorithm-driven calculation into a dynamic, context-aware dialogue. A user can prompt the LLM agent to generate a forecast that incorporates a multitude of variables that are typically impossible for traditional models to handle simultaneously. For instance, a merchandising manager could issue the following prompt: ”Generate a 4week sales forecast for SKU-456 in the Northeast region, factoring in the planned 20% price reduction in week 2, the historical sales uplift from similar promotions, and the forecasted heatwave for the region.” In response, the agent would orchestrate a multi-step process. First, it would use the RAG system to retrieve relevant data: historical sales data for SKU-456 from BigQuery, performance data for past promotions of similar products, and external weather forecast data via an API call [47]. The LLM would then reason over this rich, multi-modal context to produce a nuanced, week-byweek forecast that accounts for the interplay of these factors, far surpassing the accuracy of a simple time-series projection [30], [51]. B. Proactive Stock Level Optimization and Anomaly Detection The system automates and enhances the continuous process of stock level optimization. It can be configured to constantly monitor key performance indicators (KPIs) such as inventory turnover ratios, sell-through rates, and supplier lead times, which are calculated in near real-time from the BigQuery data warehouse [46], [54], [56]. Based on this live data, Inventory GPT can proactively calculate and recommend adjustments to safety stock levels, dynamically increasing them in response to rising demand volatility or detected supply chain disruptions to mitigate stockout risk [55]. Furthermore, the LLM’s pattern-recognition capabilities enable sophisticated anomaly detection. The system can identify deviations from expected performance, such as a sudden and unexplained drop in a product’s sell-through rate. It can then go beyond a simple alert by generating a hypothesis for the cause, grounded in retrieved data. For example, it might generate an insight such as: ”Alert: Sell-through for SKU-789 has dropped 50% weekover-week in the Western region. This coincides with the launch of competitor product XYZ, which has been heavily promoted online. Recommend an immediate promotional and pricing review for SKU-789 to remain competitive.” [52] C. Democratized Data Analytics via Natural Language Interface Perhaps the most transformative application of Inventory GPT is its ability to democratize data access. The natural language interface empowers non-technical stakeholders across the organization to perform complex data analysis without writing a single line of code or navigating complex BI dashboards [32], [58]. This breaks down the traditional barriers between business users and their data, fostering a more agile and data-driven culture. Example queries from different user personas illustrate this capability: •Store Manager: ”Which items in my store have less than two weeks of cover and are at risk of stocking out?” •Merchandiser: ”Compare the gross margin return on investment (GMROI) for our top 5 brands in the apparel category this quarter versus last quarter.” [46] •Supply Chain Executive: ”Summarize the key inventoryrelated risks across our supply chain right now, and list the top three SKUs most impacted by the recent port delays.” In each case, the agent translates the query, gathers the data, performs the analysis, and synthesizes the results into a clear, actionable response. This shifts the paradigm from ”what happened?” to ”what should we do?”. A traditional BI dashboard can show that inventory turnover is low (descriptive analytics). The Inventory GPT agent, however, can be prompted with, ”Inventory turnover for product line X is low. What are my options to improve it?”. The agent can then generate a set of prescriptive recommendations, such as suggesting a markdown strategy based on historical price elasticity data, proposing a product bundling opportunity, or recommending liquidation to free up capital. This transforms the system from a passive reporting tool into an active strategic advisor. VI. DISCUSSION AND FUTURE WORK The proposed Inventory GPT framework represents a significant leap forward in the application of AI to retail operations. However, its successful implementation and future evolution depend on a clear-eyed assessment of its potential impact, inherent challenges, and promising avenues for further research. A. Anticipated Performance Improvements The adoption of Inventory GPT is expected to drive substantial improvements across a range of critical retail inventory KPIs. By leveraging real-time data and a wider array of contextual variables, a primary benefit will be a marked increase in demand forecast accuracy, leading to a direct reduction in both stockout frequency and the carrying costs associated with overstocking [46]. The system’s ability to provide proactive recommendations for stock optimization and anomaly detection should lead to a higher inventory turnover ratio and an improved sell-through rate, indicating more efficient use of capital and better alignment with consumer demand. Ultimately, these operational enhancements are expected to improve the Gross Margin Return on Investment (GMROI), a key measure of profitability in retail [46]. B. Implementation Challenges and Considerations Despite its potential, the deployment of a system like Inventory GPT is not without challenges. •Cost and Complexity: The initial implementation requires significant investment in cloud infrastructure, data engineering, and model development or fine-tuning. The total cost of ownership must be carefully evaluated against the expected returns [41]. •Data Quality: The principle of ”garbage in, garbage out” applies with amplified force to AI systems. The framework’s effectiveness is fundamentally dependent on the availability of high-quality, clean, and consistent data streams. A substantial preliminary effort in data governance and cleansing is a prerequisite for success [22]. •Security and Privacy: Housing sensitive sales and operational data on a cloud platform necessitates robust security measures. Adherence to data privacy regulations and the implementation of strong access controls within the GCP environment are paramount to protect against data breaches [22], [41]. •Change Management and User Adoption: Transitioning from familiar spreadsheets and legacy systems to a novel, AI-driven interface requires a concerted change management effort. Employees must be trained not only on how to use the system but also on how to trust its outputs and integrate its insights into their daily workflows. Overcoming resistance to change is a critical socio-technical challenge [30], [41]. C. Future Research Directions The Inventory GPT framework serves as a foundation upon which more sophisticated capabilities can be built. Several promising directions for future work exist: •Multi-Agent Systems: The current single-agent architecture could be expanded into a collaborative multiagent system. Specialized agents—such as a ”Procurement Agent,” a ”Logistics Agent,” and a ”Marketing Agent”—could interact and negotiate with one another to achieve global supply chain optimization, balancing competing objectives like minimizing costs and maximizing sales in a more holistic manner [19]. •Multimodal Inputs: Future iterations could incorporate multimodal data streams. For example, integrating computer vision from in-store cameras could enable the system to analyze planogram compliance or detect on-shelf stock availability automatically. Similarly, ingesting data from IoT sensors on shipping containers could provide real-time, granular tracking of goods in transit. •Advanced Causal Inference: While the current system excels at identifying correlations and patterns, a key area for advancement lies in developing more sophisticated models that can infer causality. Moving beyond knowing that a promotion increased sales to understanding precisely why and by how much under different conditions would unlock a deeper level of strategic insight for retailers. VII. CONCLUSION The persistent challenges of stockouts, overstocking, and operational inefficiency in the retail sector are not isolated problems but rather systemic consequences of relying on outdated inventory management paradigms. Traditional systems, constrained by their architectural rigidity, data latency, and limited analytical capabilities, are fundamentally ill-suited for the dynamic and unpredictable nature of modern commerce. This paper has introduced Inventory GPT, a novel agentic framework that offers a transformative alternative. By architecting a solution on the Google Cloud Platform that combines a real-time data pipeline with the advanced reasoning capabilities of a Retrieval-Augmented Generation (RAG)-grounded Large Language Model, the system addresses the core deficiencies of its predecessors. The framework establishes a single source of truth, grounds its intelligence in factual, up-to-the-second enterprise data, and democratizes access to powerful analytics through an intuitive natural language interface. 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