Amazon Bedrock has quickly become one of the most talked-about platforms in the software world—a buzzword echoing across boardrooms, tech meetups, and cloud architecture discussions alike. Given its growing popularity, there’s little need to cover the basics here. Instead, let me take you into a real-world conversation that brought this technology into a sharp, business-focused context.
Just a few months ago, I caught up with a friend of mine—a successful retail business owner—who casually brought up his curiosity about integrating generative AI into his operations.
“What an incredible time to embrace it,” I responded, sensing the potential spark.
He leaned in and shared his vision:
“I want a smart solution, something like a conversational agent that can understand a customer’s buying needs, streamline the sales process, and—most importantly—if the exact product isn’t available, it should intelligently recommend alternatives based on intent, price, and market trends.”
That’s when it hit me: this wasn’t just a chatbot—this called for an agentic AI system. A solution powered by GenAI that could reason, adapt, and take action with business context in mind.
We ended up spending the afternoon diving deep into possibilities over two strong black coffees. By the time the mugs were empty, we had mapped out a high-level solution tailored to his business needs—bridging customer intent, product intelligence, and market awareness in one streamlined flow.
Later that evening, I realized the conversation was worth documenting—not just from a solution standpoint, but through the lens of business value and architectural thinking. This article is the result—a walk through that thought process, focusing on how agentic GenAI systems with AWS Bedrock can unlock real, transformative outcomes.
Business Problem
A premium bags and shoes retailer, operating across both physical stores and online channels, faces a recurring challenge: the inability to meet customer expectations when a specific product is unavailable. Whether it's an item that's out of stock or a product that doesn't quite match the customer's style or preferences, the result is the same—missed sales opportunities and (and I guess) disappointed shoppers.
This issue arises frequently at the store level and in the digital storefront. Customers often walk away or abandon their carts simply because no suitable alternatives are presented in real time. The lack of intelligent, personalized recommendations at the moment of decision-making is leading to lost revenue and a decline in customer satisfaction.
How Agentic AI Solve This Problem
Enter Generative AI and Agentic AI—transformative technologies that can revolutionize how retailers respond to product availability challenges.
By leveraging Amazon Bedrock, businesses can deploy GenAI-powered agents capable of deeply understanding customer intent, context, and preferences in real time. When a specific product is unavailable, the agent doesn’t simply show generic alternatives—it intelligently curates suggestions that align with the customer’s style, color preferences, price range, and even previous purchase history.
These agents go beyond basic chatbot interactions. They think and act like digital sales associates, capable of reasoning across a product catalog, identifying patterns, and making personalized recommendations that feel human and thoughtful. They can explain why an alternative product is a good match, handle follow-up questions naturally, and even suggest complementary items—just like an expert salesperson would.
For example, if a customer is searching for a specific designer handbag that’s out of stock, the agent can dynamically recommend visually similar models, limited-edition pieces, or items trending with similar customer profiles—all within the same conversation.
This approach not only salvages potentially lost sales but also elevates the customer experience, making shoppers feel understood and supported. The result: increased conversion rates, stronger brand loyalty, and a modern retail experience that meets the expectations of today’s digitally-savvy consumers.
High-level Flow
To bring the GenAI-powered agent to life, during the thought process of the architecture I decided to utilise a combination of AWS Bedrock, Amazon OpenSearch, AWS API Gateway, Lambda and a few other key AWS services to enable a scalable, secure, and intelligent experience.
Architecture
Architecture Components
Customer Interaction Layer
Customer Chat Interface
Description: This is the front-end application (web, mobile, smart kiosk) where customers send their queries. I have not focused in detail on these front-end applications in this context, as our primary focus must center on the Agentic AI solution.
Role: Provides the user experience for interaction with the AI agent.
Amazon API Gateway
Description: A fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale.
Role: Acts as the single entry point for all customer queries. It handles request routing, authorization, rate limiting, and acts as a proxy to the back-end Lambda functions.
Security: Integrates with Amazon Cognito for user authentication and authorization.
Query Handler Lambda Function
Description: A serverless compute service that executes code in response to events.
Role: Receives the raw customer query from API Gateway. It can perform initial validation or pre-processing of the query before passing it to the next stage.
Scalability: Automatically scales with the number of incoming requests.
Core AI & Data Processing Layer
Query Ingestion SQS Queue
Description: A fully managed message queuing service that enables us to decouple and scale microservices, distributed systems, and serverless applications.
Role: Provides asynchronous processing of customer queries. This prevents the API Gateway from timing out for longer processing queries and decouples the ingestion from the processing, improving resilience.
Product Availability Check Lambda Function
- Role: Consumes messages from the SQS queue. It queries the
DynamoDB: Product Catalog/Inventoryto check if the requested product is available. If unavailable, it extracts relevant keywords or product identifiers to initiate the search for alternatives.
Product Catalog/Inventory DynamoDB
Description: A fast and flexible NoSQL database service for single-digit millisecond performance at any scale.
Role: Stores the real-time product catalog and inventory information. This allows for quick lookup of product availability.
OpenSearch Query Handler Lambda
- Role: If a product is unavailable, this function constructs a query for Amazon OpenSearch Service. It uses the extracted keywords and potentially context from the initial query to perform both semantic and vector searches.
Amazon OpenSearch Service
Description: A managed service that makes it easy to deploy, operate, and scale OpenSearch clusters. It supports both keyword-based search (semantic) and vector search (for similarity).
Role: Stores vectorized product descriptions, attributes, and potentially customer reviews. It's crucial for identifying relevant product alternatives based on semantic similarity to the unavailable product or the customer's desired attributes.
Scalability: Easily scales to handle large product catalogs and high query volumes.
Security: Deployed within a VPC for network isolation, and access controlled via IAM policies.
Amazon Bedrock Knowledge Base
Description: A fully managed capability in Amazon Bedrock that enables you to connect foundation models (FMs) to your company data sources for Retrieval Augmented Generation (RAG).
Role: This acts as the external knowledge source for the Bedrock Agent. The prepared contextual product data is ingested here. When the Bedrock Agent receives a request, it can query this Knowledge Base to retrieve relevant product information to augment its response. This is crucial for grounding the GenAI model's responses in factual, up-to-date product data, reducing hallucinations.
Amazon Bedrock Agent
Description: A new capability within Amazon Bedrock that allows you to create generative AI applications that can automate multi-step tasks by seamlessly connecting with company systems, APIs, and data sources.
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Role: This is the core orchestrator of the GenAI solution.
- It receives the user query (potentially enhanced by initial processing).
- It understands the intent ("product unavailability, find alternatives").
- It orchestrates calls to the Bedrock Knowledge Base (via RAG) to get relevant product alternatives.
- It uses a Foundation Model (FM) (e.g., Anthropic Claude, Amazon Titan) to craft a natural, personalised, and persuasive response incorporating the retrieved product recommendations.
- Memory Retention: Bedrock Agents can maintain conversational context across interactions, leading to more human-like and continuous dialogues.
- Code Interpretation: Can be used for complex analytical queries or dynamic content generation if needed.
Response Formatter Lambda Function
- Role: Receives the generated response from the Bedrock Agent. It can perform post-processing such as formatting the response for the chat interface, adding specific links, or integrating with other downstream systems. It then sends the final response back to the customer via API Gateway.
Data Storage & Pre-processing
Product Data Source Bucket
Description: Object storage built to retrieve any amount of data from anywhere.
Role: Serves as a data lake for storing raw product data, images, descriptions, and other unstructured or semi-structured information that will be used to generate embeddings for OpenSearch. It can also store chat logs for analytics.
Security, Monitoring & Management
Amazon Cognito
Description: A service that provides authentication, authorization, and user management for web and mobile apps.
Role: Manages user identities and provides secure authentication for the chat interface, integrating with API Gateway for authorization.
AWS IAM
Description: Enables us to securely control access to AWS services and resources.
Role: Defines granular permissions for all AWS services and resources. Each Lambda function, Bedrock Agent, and other services will operate under IAM roles with the principle of least privilege, ensuring they only have access to the resources they need.
AWS KMS
Description: Makes it easy for us to create and manage cryptographic keys and control their use across a wide range of AWS services and in the applications.
Role: Used for encryption at rest for sensitive data stored in DynamoDB, S3, and OpenSearch Service, ensuring data confidentiality.
Amazon VPC
Description: Enables us to launch AWS resources into a virtual network.
Role: Provides network isolation for services like OpenSearch Service and potentially Lambda functions connecting to internal resources, enhancing security.
Amazon CloudWatch
Description: A monitoring and observability service.
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Role: Collects logs, metrics, and events from all AWS services in the architecture (Lambda, API Gateway, Bedrock, OpenSearch, DynamoDB). This is critical for:
- Traceability: End-to-end logging of requests and responses helps trace the flow of information and debug issues.
- Monitoring: Real-time metrics for performance, errors, and usage.
- Alerting: Setting up alarms for anomalies (e.g. increased error rates, high latency) via Amazon SNS.
- CloudWatch Logs Insights: For interactive analysis of log data.
Amazon Bedrock Guardrails
Description: A feature within Amazon Bedrock that allows us to implement safety policies and filters for generative AI applications.
Role: Ensures that the GenAI model's responses are safe, on-topic, and do not contain harmful, offensive, or off-brand content. It's crucial for maintaining brand reputation and compliance.
DevOps & MLOps
GitHub and GitHub Actions
Description: Services for Continuous Integration and Continuous Delivery (CI/CD).
Role: Automate the build, test, and deployment of the Lambda functions, Bedrock Agent configurations, and other infrastructure changes. This ensures rapid, consistent, and reliable deployments.
Amazon SNS
Description: A fully managed messaging service for application-to-application (A2A) and application-to-person (A2P) communication.
Role: Sends alerts based on CloudWatch alarms, notifying relevant teams of operational issues or security incidents.
Amazon CloudWatch Dashboards/Logs Insights
Description: Visualisation and query tools within CloudWatch.
Role: Provides centralised dashboards for monitoring the health and performance of the entire solution, and allows for deep dives into logs for troubleshooting and analysis.
Enterprise Grade Considerations
Scalability: All chosen AWS services (Lambda, API Gateway, SQS, DynamoDB, OpenSearch, Bedrock) are inherently scalable and managed by AWS, eliminating the need for manual server provisioning or scaling.
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Security
- Authentication & Authorization: Amazon Cognito and IAM roles with least privilege.
- Data Encryption: AWS KMS for encryption at rest and SSL/TLS for encryption in transit.
- Network Isolation: VPC for internal services like OpenSearch.
- Content Moderation: Amazon Bedrock Guardrails to prevent harmful or off-brand content.
- PII Handling: Implementation of PII redaction or masking if sensitive customer data is processed.
Traceability: Comprehensive logging with CloudWatch Logs and metrics, allowing for detailed auditing and debugging. Chat history persistence in DynamoDB also aids in traceability and understanding user interactions.
Reliability & High Availability: Services are designed for high availability by default (e.g. multi-AZ deployments for DynamoDB, OpenSearch, and Bedrock). SQS provides decoupling for resilience.
Cost Optimisation: Pay-as-you-go model for most services, and serverless compute (Lambda) means you only pay when code runs.
Observability: Integrated monitoring through CloudWatch and its various features.
There are countless enhancements that can be made to this solution, unlocking endless business possibilities. On a recent call, my friend mentioned that his development team had just delivered the first version of what they called an "intelligent agent" for their e-commerce website. I was thrilled to hear that and immediately said, "Mate, don’t just call it an intelligent agent—give it a name! I truly believe these AI applications will soon become part of human society, and just like us, they deserve names!"
Top comments (2)
Your walkthrough does a fantastic job of demystifying how to build a retail customer‑facing agent using Amazon Bedrock. I especially appreciated the clear architecture diagram and step‑by‑step implementation of the Lambda‑Backed action groups—it makes the path from idea to demo so tangible. Great piece of writing explaining tech and real‑world applicability!
Great breakdown of how Bedrock can support retail customer success.I really like how you’ve clearly outlined the role of the Bedrock agent in improving customer success in retail. The integration examples and architecture breakdown make it easier to understand the potential impact in real world scenarios. Thanks for sharing this, would love to see a follow up post with any lessons learned or metrics if this is already in use somewhere.