Building Resilient Supply Chains: Multi-Agent AI Architectures for Retail and CPG with Amazon Bedrock

It’s 2 AM. Your phone buzzes with an alert: major port closure, 47 inbound shipments affected, and a looming promotional launch in 72 hours. You quickly open your laptop to review a dozen disparate systems: inventory dashboards, logistics platforms, supplier portals. Each system tells part of the story, none providing the answer you need. How do you reroute shipments, reallocate inventory, and protect your promotional commitments before your competitors capture the market share you are about to lose?

When a disruption impacts your supply chain, such as a major port closure, every minute counts. For retail and CPG companies, supply chain disruptions from labor shortages, weather events, or unexpected port closures can result in millions of lost revenue and damaged customer relationships. Managing these disruptions and formulating responses is a manual process (even for organizations with modern data-driven interconnected supply chains). Traditional response systems struggle to process the volume of data, coordinate across multiple stakeholders, and generate actionable recommendations fast enough to matter.

The supply chain resilience challenge

Modern retail and CPG supply chains are intricate networks spanning global suppliers, distribution centers, transportation networks, and retail locations. When disruptions occur, decision-makers face several critical challenges:

• Data Fragmentation: Critical information scattered across inventory systems, logistics platforms, supplier databases, and external data sources
• Time Sensitivity: Hours matter when rerouting shipments or reallocating inventory
• Complexity: Multiple interdependent variables requiring simultaneous optimization
• Stakeholder Coordination: Suppliers, logistics partners, and internal teams need synchronized responses

Traditional approaches rely on manual analysis and sequential decision-making processes that simply can’t keep pace with the speed and complexity of modern supply chain disruptions.

Multi-Agentic AI: a new paradigm for supply chain intelligence

Multi-agent AI architectures represent a fundamental shift in how we approach complex business problems. Instead of a single AI system attempting to handle all aspects of a problem, specialized AI agents work collaboratively, each focusing on their domain of expertise while a supervisor agent orchestrates their efforts.

With the multi-agent collaboration capability of Amazon Bedrock AgentCore, now generally available, combined with the latest foundation models, organizations can build production-ready systems where specialized agents work together to address supply chain disruptions in real-time.

Architecture overview: specialized agents working in concert

Our demonstration architecture leverages both foundation models provided by Amazon Bedrock, the Agentic AI operationalization capabilities provided by Amazon Bedrock AgentCore, and multi-agent collaboration to create a resilient supply chain response system. The architecture consists of:

Supervisor Agent: Supply Chain Coordinator

• Analyzes incoming disruption alerts
• Delegates tasks to specialized agents
• Consolidates recommendations into actionable proposals
• Maintains context across the entire response workflow

Specialized Collaborator Agents:

Logistics Optimization Agent

• Evaluates alternative transportation routes
• Assesses carrier availability and capacity
• Research available transfers and their associated details
• Validates recommendations for logistics adjustments
• Generates the logistics components of the execution report

Inventory Management Agent

• Validates inputs from the disruption event and from other agents
• Performs impact analysis of the various proposed solutions
• Calculates inventory shortages and the results of proposed transfers

Promotional Risk Agent

• Analyzes impacts to products affected by disruption and any in the proposed solution
• Retrieves relevant promotional data that may impact disruption or proposed alternatives
• Provides promotional details to other agents

Shipment Tracking Agent

• Provides detail on upstream shipment delays that impact proposed adjustments
• Validates shipment destination options via feasibility review

Technical implementation with AWS services

The architecture is built on a foundation of AWS services designed for enterprise-scale AI applications:

Amazon Bedrock AgentCore provides the infrastructure for deploying and operating AI agents securely at scale, including runtime, memory, identity, observability, and API integration capabilities. Multi-agent architectures deployed to Amazon Bedrock AgentCore Runtime enable each specialized agent to:

• Execute multi-step workflows autonomously
• Connect securely to enterprise data sources through knowledge bases
• Invoke APIs and action groups for real-time data access
• Maintain conversation context and memory

Multi-Agent Collaboration allows the supervisor agent to:

• Break down complex disruption scenarios into manageable tasks
• Delegate to appropriate specialized agents
• Coordinate information flow between agents
• Consolidate outputs into comprehensive recommendations

Key Technical Features:

• Inline Agents: Dynamic adjustment of agent roles at runtime for flexible response scenarios
• Payload Referencing: Efficient data handling that reduces transfer overhead and improves response times, meaning that once the disruption event triggers the agent, the supply chain user has a data-driven, verifiable resolution plan that aligns with business objectives by the time they sit down to resolve the disruption.
• Enhanced Traceability: Comprehensive monitoring and debugging capabilities for production operations that include per-agent thoughts, customized tool interaction results, and deterministic optimization strategies that do not rely on generative AI hallucinations for business alignment.

Demo walkthrough: port closure scenario

Let’s walk through how the system responds to a real-world disruption: a major West Coast port closure affecting inbound shipments.

Step 1: Disruption Detection

The system receives an alert about the port closure, including affected shipments, estimated duration, and impacted SKUs.

Figure 1: Distribution Analysis

As seen in Figure 1, the system has identified a retail use case and a port closure as the disruption. In this scenario, a typhoon is expected to impact Singapore. The screen shows a simulation of the disruption event prompting the start of the analysis process.

Step 2: Supervisor Agent Analysis

The Supply Chain Orchestrator analyzes the disruption scope and creates a response plan, delegating tasks to specialized agents.

Figure 2: Disruption Response Strategies

Figure 2 displays the specific disruption response strategies identified by the multi-agent application. Two recommendations are shown, one full transfer that is available to cover the shipment that would be delayed by the port closure. The second recommendation is an additional proposed transfer to cover the upcoming promotional campaign. The reference figure also shows the presented option to Approve or Decline the proposed strategies, showing that the human domain experts are still involved in the process.

Step 3: Multi-Agent Optimization Strategy:

• Inventory Intelligence Agent identifies relevant distribution centers with alternative stock that can be leveraged to ensure minimal stock-outs.
• Inventory Agent retrieves and provides detail per SKU and per pallet as well as in-time and projected future inventory impact of orders.
• Promotional Risk Agent associates products with active and upcoming promotions to determine relevant promotional data that may impact the optimization strategy.
• Shipment Tracking Agent researches active and proposed shipments to validate optimizations against real-world shipment data.

Figure 3: Multi-Agent coordination

Figure 3 shows each of the specialized agents along with their respective tasks with the interaction strategy for the Retail scenario. The supply Chain Coordinator agent, the Logistics Agent, the Inventory Agent, the Promotional Risk Agent, and the Shipment Tracking agent are in the multi-agent coordination.

Step 4: Consolidated Recommendations

The supervisor agent synthesizes the findings for this scenario into three data-driven proposals:

Immediate Reallocation: Redistribute existing inventory from low-demand regions

Alternative Routing: Reroute shipments through Gulf Coast ports with 3-day delay

Supplier Acceleration: Engage backup suppliers for critical SKUs with 5-day lead time

Each proposal includes cost implications, timeline estimates, and risk assessments, all generated within minutes of the initial disruption alert.

Figure 4: Figure 4 shows the impact summary based on the Approved.

Shown is the impact summary based on the Approved strategies for the identified scenario, a Port Closure. The multi-agent application has determined that the accepted proposals will result in -$4,275 in transportation costs, and $28,500 in total revenue preserved. Also shown are both Approved transfer orders with the order numbers, the units per order, the item ID, the distribution centers, and the arrival dates.

Business impact and measurable outcomes

Organizations implementing multi-agent AI architectures for supply chain resilience are seeing significant benefits:

Speed: Response time reduced from hours to minutes for complex disruption scenarios

Accuracy: Data-driven recommendations eliminate guesswork and reduce costly errors

Scalability: Handle multiple simultaneous disruptions without additional headcount

Transparency: Complete audit trail of decision-making process for compliance and learning

The multi-agent approach also enables continuous improvement: each disruption response is retained in Amazon Bedrock Agent Core Memory as a discrete long-term memory strategy, refining agent performance and expanding capabilities. Auditors and compliance can review the agentic processes, note decision methods, and review the memory to ensure all decisions have been made according to existing policies via Amazon Bedrock AgentCore Policy.

Amazon Bedrock AgentCore provides the production-grade infrastructure needed to move from prototype to production, with built-in security, scalability, and observability.

Conclusion: the future of supply chain resilience

Supply chain disruptions are inevitable, but their impact doesn’t have to be. Multi-agent AI architectures, powered by Amazon Bedrock AgentCore, represent a fundamental advancement in how retail and CPG companies respond to complexity and uncertainty.

By enabling specialized AI agents to work collaboratively on complex problems, organizations can transform supply chain disruptions from crises into manageable events with clear, data-driven response paths.

This technology is production-ready today. The question for technical leaders is not whether to adopt multi-agent AI for supply chain resilience, but how quickly you can implement it to protect your business from the next disruption.

Ready to explore multi-agent AI for your supply chain? Visit the AWS Industries Retail & Consumer Goods space at NRF 2026: Retail’s Big Show booth 4438 to see the demo in action. You can also review the Amazon Bedrock AgentCore documentation to learn more about building production-ready multi-agent systems or contact your AWS account team to discuss your specific supply chain challenges.