Amazon Bedrock enables developers to integrate powerful generative AI models without the complexity of model training, infrastructure management, or scaling. You can build intelligent multi-agent applications that process and respond to user queries using advanced AI techniques efficiently.
In this Cloud Lab, you’ll begin by creating and populating a dataset in an Amazon S3 bucket. This dataset will serve as the source for hotel and restaurant information that your agents will use to generate recommendations.
Next, you’ll create two Bedrock Agents: the hotel and restaurant agents. Each agent will be configured with its own action group, which links the agent to an AWS Lambda function. These Lambda functions will be responsible for retrieving, filtering, and returning relevant results from the dataset in S3. The Hotel Agent will process queries based on city and budget, while the restaurant agent will focus on city and cuisine.
After setting up specialized agents, you’ll create a supervisor agent that coordinates requests and determines when to invoke the hotel agent, the restaurant agent, or both in parallel. This ensures that user queries are intelligently routed and handled by the appropriate agents.
Finally, you’ll integrate the supervisor agent with a simple frontend web application, enabling users to submit natural language queries and receive clear, structured recommendations. The supervisor agent will orchestrate agent collaboration, merge the results, and deliver concise responses back to the frontend in real time.
After completing this Cloud Lab, you’ll have the skills to use Amazon Bedrock to create multi-agent systems, integrate them with AWS services like S3 and Lambda, and connect them to a frontend application. You will also gain hands-on experience building agent collaboration workflows and designing a recommendation engine powered by generative AI.
The following is the high-level architecture diagram of the infrastructure you’ll create in this Cloud Lab:
