Guidance for Multi-Agent Orchestration on AWS
Overview
This Guidance demonstrates how to effectively orchestrate multiple specialized AI agents to solve complex customer support challenges through different coordination mechanisms on AWS. Modern customer service environments demand sophisticated handling of multi-step interactions, personalized responses, and seamless access to various data sources. This Guidance provides multiple architectural patterns, or reusable approaches to these design challenges, each tailored to specific requirements for development speed, control, customization, and human involvement, showcasing three coordination mechanisms: Amazon Bedrock multi-agent collaboration, Agent Squad microservices, and LangGraph workflow orchestration. Organizations can leverage these specialized AI coordination approaches to build robust, scalable customer support systems that combine AI efficiency with appropriate human oversight.
How it works
Amazon Bedrock multi-agent collaboration
This architecture showcases how a supervisor agent orchestrates multiple specialized sub-agents through Amazon Bedrock's native collaboration feature for comprehensive business scenarios. This pattern automatically handles task delegation and response aggregation across various functional agents with enterprise-grade reliability and built-in monitoring.
Agent Squad
This architecture diagram shows how specialized AI agents operate as independent microservices, each handling specific business domains through custom coordination logic. This pattern enables complete control over agent behavior and seamless integration with both external systems and human agents through message queues.
LangGraph
This architecture diagram shows a LangGraph-powered supervisor agent running on Amazon ECS that intelligently coordinates four specialized sub-agents through LangGraph’s agent orchestration framework, enabling seamless task delegation, context sharing, and response synthesis across distributed agents for comprehensive customer support scenarios.
Deploy with confidence
Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs.
Go to sample code:
Benefits
Accelerate customer service with intelligent routing
Deploy specialized AI agents that automatically direct inquiries to the right knowledge source. Focus on delivering personalized responses while the system handles complex orchestration behind the scenes.
Scale AI interactions without infrastructure management
Implement a serverless architecture that automatically adjusts to demand fluctuations. Eliminate capacity planning while maintaining consistent performance during peak customer engagement periods.
Enhance decision quality with comprehensive data access
Connect AI agents directly to both structured and unstructured data sources across your organization. Deliver responses that combine real-time database insights with contextual knowledge base information.
Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.
Did you find what you were looking for today?
Let us know so we can improve the quality of the content on our pages