Multi-Agent AI for CPG Operations: A Vendor-Neutral Guide to Amazon Bedrock Architecture

Multi-Agent AI for CPG Operations: A Vendor-Neutral Guide to Amazon Bedrock Architecture As of May 23, 2026, the multi-agent collaboration capability of Amazon Bedrock AgentCore is generally available, enabling operations leaders to build specialized agent systems without vendor lock-in. For consumer packaged goods (CPG) companies, this capability opens a practical path to combine multiple AI models—each tuned for a distinct task—into a cohesive operations workflow. This article presents a three-agent architecture deployed in a pilot with a mid-size CPG manufacturer, covering design, model selection, agent handoff patterns, cost benchmarks, and performance results. Why Multi-Agent AI for CPG Operations? CPG operations are inherently multi-step and interdependent. Demand forecasting influences shelf allocation, which in turn affects promotional compliance. Traditional monolithic AI models

struggle to handle the distinct data formats, update frequencies, and business rules across these stages. A multi-agent architecture lets you assign a specialized agent to each domain—demand, shelf, promotions—and orchestrate their collaboration via standardized handoff protocols. The key advantages include: - Domain specialization : Each agent uses the best model and data pipeline for its task. - Independent scalability : Update or replace one agent without retraining the whole system. - Explainability : Each agent’s reasoning can be audited separately. - Flexibility : Choose models from different providers to avoid lock-in. Architecture Overview: Three Specialized Agents The pilot system consisted of three agents, all running on Amazon Bedrock with multi-agent collaboration enabled: 1. Demand Forecasting Agent – Powered by Qwen 3.8 Max. This agent ingests historical sales data, season

al patterns, and external signals (e.g., weather, holidays). It outputs a 13-week forecast at the SKU-store-week level. 2. Shelf Allocation Agent – Powered by Llama 4. This agent takes the forecast and retailer-specific shelf constraints (facings, pack-out quantities, adjacency rules) to produce an optimized shelf plan per store. 3. Promotional Compliance Agent – A lightweight rule-based agent using Amazon Bedrock’s native model (Claude 3.5 Haiku) plus a custom governance engine. It validates shelf plans against promotion calendars, flagging non-compliant placements and suggesting corrections. Each agent was deployed as a separate Bedrock agent with its own knowledge base and action groups, connected through the AgentCore multi-agent collaboration framework. Communication between agents used a structured JSON schema for handoff, ensuring data consistency. Agent Handoff Patterns: From For

ecasting to Shelf Allocation Agent handoff is the critical path in this architecture. The pilot implemented a sequential handoff pattern: - Step 1 : Demand Forecasting Agent completes its run and emits a event containing top 10 SKUs by demand, average weekly volume, and confidence intervals. - Step 2 : The multi-agent coordinator passes the to the Shelf Allocation Agent along with the store’s current shelf plan. - Step 3 : Shelf Allocation Agent runs its optimization and outputs a event. - Step 4 : Promotional Compliance Agent receives the proposal and the active promotion calendar, then returns a with any violations. - Step 5 : If violations exist, the system loops back to Step 2 with adjusted constraints; otherwise, the final shelf plan is approved. This pattern ensured that downstream agents always had the latest upstream output without manual data glue. Latency for each full cycle av

eraged 4.2 seconds for a typical 200-SKU store—acceptable for daily replanning. Model Selection: Qwen 3.8 Max for Demand, Llama 4 for Shelf Allocation Model choice directly affects cost, accuracy, and latency. In the pilot, we selected: - Qwen 3.8 Max (via Bedrock) for demand forecasting because of its strong performance on time-series reasoning and its ability to handle large context windows (128K tokens). It processed 5 years of weekly SKU data per store in under 1.5 seconds. Official pricing was $0.80 per 1M input tokens (May 2026). - Llama 4 (via Bedrock) for shelf allocation because of its performance on constraint-satisfaction tasks and its lower cost per output token ($0.30 per 1M output tokens). Llama 4’s instruction-following accuracy for retail display rules was validated against 20 store-specific guides. - Claude 3.5 Haiku (Bedrock native) for the promotional compliance agent

because of its fast inference (under 500ms per validation) and low cost ($0.25 per 1M input tokens). This combination avoided single-vendor reliance while leveraging Bedrock’s unified API for model access and multi-agent orchestration. Cost-Per-SKU Benchmarks from a Mid-Sized Manufacturer Pilot The