Multi-Agent Inventory Optimization: A Blueprint from a 10-Retailer Pilot

Why Multi-Agent Systems for Retail Inventory? Retail inventory management has long struggled with the dual challenge of stockouts and excess inventory. Traditional systems—often rule-based or single-model AI—fail to adapt to demand volatility, supply chain disruptions, and multi-echelon complexities. Multi-agent systems offer a paradigm shift: specialized agents collaborate to handle distinct tasks, from demand forecasting to replenishment planning, while sharing context and dynamically adjusting decisions. For B2B leaders, the appeal is clear. A multi-agent system deployment can address conflicting objectives (e.g., minimizing stockouts vs. lowering carrying costs) by allowing each agent to optimize for its own metric within an overarching coordination framework. The recent pilot demonstrates how this architecture can deliver measurable operational improvements in a real-world retail se

tting. The Pilot: 10 Retailers, AWS Bedrock, and Two Specialized Models The consortium brought together 10 major retail chains from North America and Europe, each contributing anonymized sales, inventory, and supply chain data. The technical infrastructure was built on AWS Bedrock, which provides managed APIs for foundation models and simplifies multi-agent orchestration. The pilot selected two complementary agents: Demand Forecasting Agent : Powered by Qwen 3.8 Max (from Alibaba Cloud) – a large language model optimized for time-series and pattern recognition, released in January 2026. Replenishment Planning Agent : Powered by Llama 5 (from Meta AI) – a generative model fine-tuned for constrained optimization and decision-making, released in March 2026. Each agent ran as a separate Bedrock inference endpoint, communicating via a shared coordination layer built with AWS Lambda and Step F

unctions. The pilot ran for six months across 1,200 SKUs per retailer, covering both high-velocity and seasonal items. Architecture Overview: Demand Forecasting with Qwen 3.8 Max The demand forecasting agent ingested historical sales data, promotional calendars, weather data, and social sentiment signals. Qwen 3.8 Max was chosen for its strong performance on multivariate time-series benchmarks and its ability to handle long context windows—crucial for capturing seasonal patterns and trend shifts. The agent output probabilistic demand distributions for each SKU-location combination, with 14-day and 30-day horizons. Key integration details: Data was preprocessed using AWS Glue and stored in S3. The model was accessed via Bedrock’s Converse API with a custom prompt template that included recent sales spikes and inventory levels. Forecast confidence intervals were sent to a shared Redis cach

e for the replenishment agent to consume. According to Alibaba Cloud’s documentation, Qwen 3.8 Max achieves state-of-the-art results on the M5 forecasting competition, making it a strong candidate for demand forecasting use cases. Replenishment Planning with Llama 5: Closing the Loop The replenishment planning agent received the demand forecasts from the forecasting agent, along with current on-hand inventory, lead times from suppliers, and cost parameters (holding cost, stockout cost). Llama 5, fine-tuned from Meta’s general-purpose model, was specifically optimized for constrained optimization tasks using reinforcement learning from human feedback (RLHF) on supply chain scenarios. This agent produced recommended replenishment orders—including order quantities, timing, and safety stock adjustments—for each SKU. It also generated natural-language explanations for each recommendation, ena

bling inventory managers to override decisions when necessary. The agent’s outputs were validated against a rule-based baseline before being fed into the retailers’ ERP systems via AWS EventBridge. Meta’s release notes for Llama 5 emphasize its improved reasoning and planning capabilities, which were critical for handling the multi-constraint replenishment problem. Results: 25% Stockout Reduction and 18% Lower Carrying Costs Across the consortium, the multi-agent system delivered the following outcomes compared to the retailers’ existing inventory management processes: Metric Baseline Pilot Result Improvement :--------------------------------------------------------- :------- :----------- :------------ Stockout rate (percentage of SKU-days out of stock) 8.2% 6.1% 25% reduction Carrying cost as a percentage of inventory value 14.5% 11.9% 18% reduction Forecast accuracy (mean absolute perc

entage error) 22% 16% 27% improvement Planner override rate 40% of recommendations 12% of recommendations 70% reduction in manual intervention Importantly, the improvements were consistent across retailers of different sizes and categories—though the exact magnitude varied by merchandise type. The c