Amazon Bedrock with AI Agents

Name: Amazon Bedrock with AI Agents
Rating: 4.1 (11 reviews)

Get up and running with Generative AI on AWS, creating text, images, AI Agents and integrating with software.

Created byDan Currotto

Last updated 9/2025

English

English [Auto],

What you'll learn

Gain a clear understanding of Amazon Bedrock, and how to successfully use it.
Understand the difference between Generative AI and Regular AI .
Be able to navigate the AWS Bedrock console and Playground, to generate text, chat, and images.
Be able to integrate Amazon Bedrock APIs into a Python software application, generating text and images.

Course content

7 sections • 35 lectures • 3h 56m total length

Course Introduction3:38
To Get the Most out of this Course1:40
Follow these quick tips to get the most out of the course.
Course Outline2:52
You'll get a clear overview of the course.
The AI Opportunity3:39
Explore Amazon Bedrock's generative AI opportunities by highlighting the strong demand for AI talent and top salaries across roles like ml engineers and tech leads.
Python and Boto3, the AWS SDK12:37
Learn to use boto3, the AWS SDK for Python, to connect to S3, create a bucket, upload files, and list objects with the response.
Course Resources1:54
Download and unzip the bedrock resources, open in VS Code, and explore python scripts for image and text requests plus the readme with aws cli guidance.

Setting up Bedrock0:58
Creating an AWS Account17:17
Create and secure an AWS account by configuring a root user, then set up an IAM user with admin access, enable multi-factor authentication, and verify identities.
Setting Up AWS CLI and Programmatic Access8:25
Install the AWS CLI, set up IAM user access keys, configure with aws configure, and verify access by listing S3 buckets in us-east-1 with JSON output.
Foundation Model Access7:48
Learn how to request access to foundation models in Amazon Bedrock, including managing model access, selecting models, submitting use case details for Anthropic models, and troubleshooting access errors.
Foundation Model Pricing9:24
Explore Amazon Bedrock pricing across on demand, batch mode, provision throughput, and model customization, with token costs and examples like Jurassic 2 Mid and Ultra.

Amazon Bedrock Playgrounds Overview Part 16:22
Explore Amazon Bedrock playgrounds—text, image, and chat—learn to select models and run prompts, with examples comparing Python and Java and generating a festive Chihuahua image.
Amazon Bedrock Playgrounds Overview Part 26:20
Explore the chat playground in Amazon Bedrock, run llama 2 chat 13b with step-by-step prompts, and adjust temperature and top_p to observe how inference parameters shape answers to math prompts.
Differences between Text and Chat Playgrounds2:25
Compare Bedrock text playground and Bedrock chat playground, highlighting interface, input methods, context handling, capabilities, and use cases for code testing versus dialogue.
Prompt Engineering Introduction2:25
Explore prompt engineering and how text and image prompts drive generative AI results. Highlight productivity gains: up to 17% for top performers and 43% for lower performers.
Prompt Engineering for Images10:02
Prompt Engineering for Text17:45
Inference Parameters4:17
Foundation Models and Providers5:11
Foundation Model Examples3:57
Explore the Amazon Bedrock examples pane by filtering by model, modality, category, and provider. View prompts and API request, then run Amazon Titan Text G1 express in playground.

Python Introduction and Setup4:55
Integrating Bedrock with Python Part 18:00
Master integrating Amazon Bedrock with Python by using boto3 to invoke Bedrock models with prompts, handle json responses, and write outputs to files in a hands-on workflow.
Integrating Bedrock with Python Part 27:37
Integrates Bedrock with Python using boto3 to invoke the Bedrock runtime, process base64 image artifacts, and display results in an HTML page.

AI Agents Introduction10:07
Explore Amazon bedrock agents and agentic AI, using action groups, knowledge bases, and memory to autonomously handle multi-step tasks like booking one-day tours in Amman via Rag.
AI Agents Hands On3:34
Showcase building and managing agentic ai agents in amazon bedrock using agent builder, linking to python and lambdas, configuring versions and aliases, and managing knowledge bases, cost considerations, and guardrails.
Knowledge Base Preparation5:43
Action Groups Part 115:55
Create read and upsert lambdas and connect them to a bedrock agent's action groups to enable tour bookings from available slots.
Action Groups Part 213:31
Develop and test a second action group, tour booking upsert, wiring it to a Lambda with an OpenAI API schema and DynamoDB storage, and prepare a Rag knowledge base.
Knowledge Base15:24
Create a knowledge base with an S3 data source and vector store using Titan text embeddings v2 on Amazon Bedrock; index a PDF and enable question answering and booking flows.
Connecting to Software (Python)7:20
Connect an Amazon Bedrock AI agent to Python using Boto3 to send messages with an agent id, alias id, and region. Demonstrate alias creation and Jerash tour bookings.
Clean Up !!3:26
Costs for AI Agents Hands On2:08

Requirements

Basic cloud experience is helpful but not required.
Basic programming experience is helpful but not required.

Description

This course will get you up and running quickly on Amazon Bedrock and Generative AI. Whether you are a developer, DevOps/MLOps engineer, business analyst, scrum master, or manager, this course is for you.

Start with no experience or understanding of Generative AI and leverage your current skillset or knowledge base to begin creating with Generative AI.

Understand and quickly take advantage of the incredible opportunity of Generative AI, that is quickly moving towards us.

If you are already using another Generative AI framework, rapidly move over and start using Bedrock, from AWS, the world's #1 cloud provider.

This course covers:

The incredible upcoming opportunity of Generative AI
The Difference between Regular AI and Generative AI
Overview of Amazon Bedrock
AWS Bedrock Setup, Pricing, and Model Access
Creating an AWS Account
Installing the AWS CLI and getting Programmatic Access to access the APIs
Foundation Model Access
Foundation Model Pricing
AWS Bedrock Playgrounds including Text, Image, and Chat Playgrounds
Differences between Text Playground and Chat Playground
Inference Parameters
Foundation Models
Foundation Model Examples
Python Introduction and Setup
Integrating AWS Bedrock APIs into a software application using Python
AI Agents

This is a hands-on course created to help the user to quickly understand and become productive with Amazon Bedrock and Generative AI.

Take advantage of the incredible Generative AI opportunity and get this course now.

Who this course is for:

Developers and DevOps engineers that want to leverage their current skills to get into Artificial Intelligence or MLOps.
Business analysts, scrum masters, or managers, who want to get a great understanding of Generative AI and how to use it with their teams to create and deliver software.

Amazon Bedrock with AI Agents

What you'll learn

Explore related topics

Course content

Introduction6 lectures • 26min

Amazon Bedrock Overview2 lectures • 9min

Hands On Amazon Bedrock Setup5 lectures • 44min

Amazon Bedrock Playgrounds9 lectures • 59min

Hands On: Using Bedrock with Python3 lectures • 21min

AI Agents9 lectures • 1hr 17min

Conclusion1 lecture • 1min

Requirements

Description

Who this course is for: