How a 100-Store Chain Cut Out-of-Stock by 30% with a Multi-Agent System for Retail Inventory

The Retail Out-of-Stock Problem and the Three-Agent Solution As of May 23, 2026, retail inventory management remains one of the most costly operational challenges for multi-store chains. Out-of-stock incidents alone cost the industry hundreds of billions annually in lost sales and customer churn. Traditional rule-based replenishment systems struggle to adapt to demand variability, promotional lifts, and supply chain disruptions. Enter the multi-agent system for retail inventory . This approach decomposes the inventory problem into three specialized agents that communicate and coordinate to make faster, more accurate decisions. In a recent pilot at a 100-store regional grocery chain, a vendor-neutral three-agent system built on AWS Bedrock delivered: 30% reduction in out-of-stock incidents 15% improvement in inventory turnover Lower carrying costs without sacrificing fill rates This guide

walks through the architecture, benchmarks, and how to replicate the pattern—without locking into a single vendor or model. How to Design a Multi-Agent System for Retail Inventory Management Before diving into each agent, it helps to understand the overall flow. The system operates in a cyclic loop, typically triggered every 6 hours or when a significant demand signal changes: 1. Demand Forecasting Agent (Llama 5) predicts next 48 hours of store-level demand and flags out-of-stock risks. 2. Inventory Allocation Agent (Qwen 3.8 Max) determines the optimal distribution of available stock across stores, considering capacity and constraints. 3. Replenishment Scheduler Agent translates the allocation plan into purchase orders and delivery schedules, fine-tuned on historical execution data. This three-agent architecture was chosen because single-model systems either lacked the domain depth fo

r each task or introduced latency that made real-time reshuffling impractical. The separation of concerns allowed each agent to be optimized independently. Agent 1: Demand Forecasting with Llama 5 Model: Meta Llama 5 (70B parameter variant) via AWS Bedrock Role: The demand forecasting agent ingests point-of-sale data, weather forecasts, local event calendars, and historical seasonality to produce a probabilistic demand distribution for each SKU-store combination over the next two days. Key design choices: The agent was prompted with a structured JSON schema that included store ID, SKU category, recent 7-day sales, and a confidence interval. Llama 5's large context window (128K tokens) made it feasible to process all relevant features without truncation. The model was not fine-tuned but used in a zero-shot chain-of-thought mode, outputting both a point forecast and a low/high range. This

gave the allocation agent a risk-aware input. In the pilot, the demand forecasting agent achieved a mean absolute percentage error (MAPE) of 11% at the store-SKU level—considerably better than the 18% baseline from the previous regression model. Why Llama 5? Meta’s Llama 5 demonstrated superior performance on temporal reasoning tasks compared to other open-weight models when evaluated on the pilot’s proprietary dataset. Its per-token pricing on Bedrock (approx. $0.10 per 1M input tokens for Llama 5 70B as of May 2026) was also competitive, given the high daily inference volume across 100 stores. Agent 2: Inventory Allocation with Qwen 3.8 Max Model: Alibaba’s Qwen 3.8 Max (latest instruction-tuned variant) via AWS Bedrock Role: The inventory allocation agent receives the demand forecast from Agent 1, along with current on-hand inventory and inbound shipment status, and outputs an optimal

redistribution plan. It decides which stores should reroute surplus stock to stores at risk of out-of-stock, respecting truck capacities and transfer costs. Why Qwen 3.8 Max? Qwen 3.8 Max specializes in constraint-based optimization reasoning. Its 32K context window allowed it to handle the full store network data in a single prompt. The pilot found that Qwen 3.8 Max matched or exceeded the allocation quality of a custom integer programming solver while being 4x faster to iterate on—critical for the 6-hour cycle. Integration: The demand forecasting agent writes its output to a shared state store (Amazon DynamoDB). The allocation agent reads that state, applies business rules (e.g., “never allocate more than 80% of a store’s shelf capacity”), and generates a set of transfer orders. These orders are then passed to the third agent. Agent 3: Fine-Tuned Replenishment Scheduler Model: A fine-

tuned version of a smaller open-weight base model (e.g., Llama 3.1 8B) deployed on Bedrock with a custom adapter. Role: The replenishment scheduler agent converts allocation decisions into practical logistics: it sets purchase order quantities for each SKU, delivery windows, and supplier communicati