Getting Started with Amazon Bedrock Agents

Getting Started with Amazon Bedrock Agents
Getting Started with Amazon Bedrock Agents

CLOUD LABS



Getting Started with Amazon Bedrock Agents

In this Cloud Lab, you’ll learn about agents for Amazon Bedrock and how they enhance large language models (LLMs) by managing context, executing actions, and seamlessly integrating intelligent applications with real-world functionalities.

11 Tasks

beginner

2hr

Certificate of Completion

Desktop OnlyDevice is not compatible.
No Setup Required
Amazon Web Services

Learning Objectives

A solid understanding of Bedrock Agents and their capabilities
Hands-on experience creating and interacting with Bedrock Agents to solve real-world limitations of LLMs
The ability to integrate Bedrock Agents into applications for enhanced functionality
Hands-on experience using action groups to extend the functionality of agents and interact with external systems

Technologies
Bedrock
Lambda logoLambda
DynamoDB logoDynamoDB
Cloud Lab Overview

AI has revolutionized how we build smarter and more efficient systems, with large language models (LLMs) at the forefront of this transformation. Thanks to their advanced natural language processing capabilities, these models excel at understanding context and generating meaningful responses. However, they have inherent limitations—they cannot access real-time knowledge or interact with external systems. Amazon Bedrock Agents provide the perfect solution to these challenges and empower LLMs to take intelligent actions. By enhancing LLMs with features like action execution and external system interaction, Bedrock Agents unlock the potential for building truly dynamic and intelligent applications.

In this Cloud Lab, you’ll explore the power of Amazon Bedrock Agents and their ability to significantly enhance the capabilities of large language models (LLMs) by building and improving an application step by step. You’ll begin by creating essential resources like IAM roles and DynamoDB tables that’ll be used for access control and application storage. After that, you’ll develop an application integrated with an LLM provided by Bedrock to showcase the value of AI in applications. Next, you’ll replace this direct interaction with a Bedrock Agent, making the workflow more structured and efficient. Finally, you’ll introduce action groups by integrating a Lambda function, enabling the agent to perform a real-world task by interacting with an external system, a DynamoDB table. Through this progression, you’ll see how Bedrock Agents make AI-powered applications cleaner, more efficient, and more powerful.

By the end of this Cloud Lab, you’ll clearly understand how to integrate Bedrock’s LLMs into applications, streamline workflows with agents for improved efficiency, and expand your application’s capabilities using action groups. You’ll also gain hands-on experience structuring AI-driven workflows, enabling more intelligent and dynamic interactions within your applications.

Here’s a high-level architecture diagram of the infrastructure that you’ll create in this lab:

Application powered by Bedrock Agent
Application powered by Bedrock Agent

Cloud Lab Tasks
1.Introduction
Getting Started
2.Create the Pre-Required Resources
Create IAM Roles
Create DynamoDB Tables
3.Use LLM Provided by Bedrock in an Application
Enable the Bedrock Model Access
Use LLM in the Application
4.Integrate Bedrock Agent into the Application
Create a Bedrock Agent
Integrate Agents in an Application
5.Enable the Agent to Take Actions
Create Resources for the Action Group
Use Action Groups to Perform Real-World Actions
6.Conclusion
Clean Up
Wrap Up
Labs Rules Apply
Stay within resource usage requirements.
Do not engage in cryptocurrency mining.
Do not engage in or encourage activity that is illegal.

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Use the following content to review prerequisites or explore specific concepts in detail.

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