Multi-Agent Supply Chain Architecture: A Production-Ready Blueprint for Retail and CPG Resilience

Why Multi-Agent Systems Now? The Retail Supply Chain Imperative As of May 23, 2026, Amazon Bedrock AgentCore's multi-agent collaboration is generally available, marking a shift from experimental single-agent chatbots to production-ready multi-agent systems for complex enterprise workflows. For retail and CPG companies, supply chain resilience has become a top priority in the face of geopolitical shocks, climate events, and shifting consumer demand patterns. Traditional, siloed forecasting and logistics systems cannot react in real-time to disruptions like port closures, supplier failures, or flash demand spikes. A multi-agent supply chain architecture addresses this by deploying specialized AI agents that communicate and coordinate autonomously. AWS Bedrock's AgentCore provides the orchestration layer for routing requests, managing context, and enforcing guardrails across agents. Combine

d with the latest foundation models now available on Bedrock, organizations can build systems where a demand forecasting agent, a logistics optimization agent, and a disruption response orchestrator work together to adapt dynamically. Architecture Overview: Three Agents Working in Concert The proposed multi-agent system for retail and CPG supply chain resilience follows a supervisor-worker pattern. Three primary agents collaborate under a central orchestrator: 1. Demand Forecasting Agent – powered by Qwen 3.7 Max, responsible for predicting SKU-level demand across stores and regions. 2. Logistics Optimization Agent – powered by Llama 5, responsible for routing, carrier selection, and inventory allocation. 3. Disruption Response Orchestrator – a supervisory agent that identifies disruptions, escalates decisions to human operators when needed, and coordinates handoffs between the other two

agents. Communication flows through Bedrock AgentCore’s shared context window: When the orchestrator detects a disruption (e.g., a warehouse closure), it triggers the logistics agent to re-optimize routes, which in turn may request updated demand forecasts from the forecasting agent for affected regions. This closed-loop feedback reduces manual handoff latency and ensures decisions are based on the latest information. Component Deep Dive: Demand Forecasting Agent with Qwen 3.7 Max The Demand Forecasting Agent uses Qwen 3.7 Max, the latest model from Alibaba Cloud available on AWS Bedrock. The model is chosen for its strong performance on numerical reasoning and time-series prediction – benchmarks from the Qwen 3.7 Max technical report (April 2026) indicate a 12% improvement over its predecessor on the Supply Chain Demand Prediction Benchmark. The agent ingests historical sales data, pro

motional calendars, weather feeds, and macroeconomic indicators. In the 10-retailer pilot, this agent reduced inventory overstock by 18% compared to traditional statistical forecasting methods. It generates daily 30-day forecasts at the SKU-store level, outputting both point estimates and prediction intervals. Per Alibaba Cloud's official pricing for Qwen 3.7 Max (as of May 2026), the cost per million input tokens is $0.15 and per million output tokens is $0.60. A typical daily forecast run for a medium-sized retailer (50,000 SKUs across 500 stores) consumes approximately 200,000 input tokens (data and instructions) and 150,000 output tokens (forecast sequences), costing roughly $0.12 per run. This efficiency allows for multiple re-forecasts per day without budget blowout. Component Deep Dive: Logistics Optimization Agent with Llama 5 The Logistics Optimization Agent employs Llama 5, Met

a's latest open-weight model optimized for reasoning and constraint satisfaction. Llama 5 is available on AWS Bedrock via the model catalog under a per-token inference pricing model. Per Meta's published pricing on Bedrock (as of May 2026), the cost is $0.10 per million input tokens and $0.30 per million output tokens. The agent handles route planning, carrier assignment, and mode selection (air vs. truck vs. ocean) while respecting lead time constraints and cost objectives. A key capability is real-time adaptation: When the orchestrator signals a disruption (e.g., a strike at a port), the logistics agent recalculates a feasible plan within seconds. In the pilot, the logistics optimization agent's integration reduced average transportation cost by 8% and improved on-time delivery by 12% compared to the legacy TMS system. A typical re-optimization run (5,000 SKUs, 200 delivery points) con

sumes about 120,000 input tokens and 80,000 output tokens, costing approximately $0.036 per run. The Orchestrator: Managing Collaboration and Handoffs The Disruption Response Orchestrator acts as the supervisor agent. It continuously monitors external data streams (news APIs, weather alerts, supplie