Agents for Amazon Bedrock RetailAgent

Inspiration

I was inspired by the growing need in retail for personalized, always-available customer support. Shoppers want quick answers about their orders and tailored product suggestions, but many businesses struggle to scale human support efficiently. This motivated me to build an AI-powered retail agent that delivers seamless, scalable, and intelligent shopping assistance.

What it does

It uses Amazon Bedrock Agents to power natural conversations and integrates with retail APIs to deliver accurate, real-time responses.

How we built it

I used Amazon Bedrock Agents with a foundation model to power conversations. An OpenAPI schema defined the retail APIs, and Lambda functions handled tasks like order lookup, product search, and placing orders. The agent followed the ReAct framework (reasoning + action) to break down requests, call APIs, and return accurate responses. I deployed everything with CloudFormation, tested in the Bedrock console, and ensured scalability with a serverless design.

Challenges we ran into

Integrating multiple AWS services (Bedrock, Lambda, API Gateway, DynamoDB) smoothly. Preventing LLM hallucinations by grounding responses with real product and order data. Designing secure IAM roles with least privilege. Balancing latency and scalability in a serverless setup. Debugging agent reasoning steps in the ReAct framework

Accomplishments that we're proud of

Successfully built a retail AI agent that can track orders, search products, and give recommendations in real time. Integrated multiple AWS services (Bedrock, Lambda, DynamoDB, API Gateway) into a seamless, serverless workflow. Learned how to design and deploy Bedrock Agents with custom action groups. Improved reliability by grounding LLM responses in actual retail data. Created a solution that is scalable, secure, and hackathon-ready.

What we learned

How to design and deploy Amazon Bedrock Agents with action groups and schemas. Best practices for serverless integration using Lambda, API Gateway, and DynamoDB. Techniques to reduce LLM hallucinations by grounding responses in real data. The importance of IAM security and least-privilege roles in multi-service projects. How to apply the ReAct prompting framework for reasoning + action execution

What's next for Agents for Amazon Bedrock RetailAgent

Add multi-language support to reach global customers. Integrate Amazon Personalize for smarter product recommendations. Expand into voice support with Amazon Connect. Replace the prototype SQLite DB with DynamoDB for production scalability. Enhance analytics and monitoring to track customer interactions and agent performance.

Built With

• action
• agents
• amazon
• amazon-api-gateway
• amazon-cloudformation-database:-sqlite-(prototype)
• amazon-dynamodb-(scalable-option)-apis:-custom-retail-apis-(order-lookup
• amazon-web-services
• aws-lambda
• bedrock
• json-(api-schema)-frameworks:-react-prompting-framework-cloud-services:-amazon-bedrock
• languages:-python
• product-catalog
• recommendations)-other:-iam-for-security