AWS Agentic AI Foundations

Code: awsaaif

What you'll learn

On this one-day course, you'll explore the core principles and strategies for designing Agentic AI systems using AWS services. You'll learn how Agentic AI differs from traditional conversational systems, and how to use tools like Amazon Q, Kiro, Amazon Bedrock Agents, and Amazon Bedrock AgentCore to build autonomous, goal-driven solutions that solve real-world problems.

The aim of this course is to understand the landscape, patterns, and tooling instead of building full production systems.

You will learn how modern AI systems have moved beyond simple chatbots into goal-driven agentic systems, as well as:

The core ideas behind agentic AI and explanations of how agents differ from standard generative AI tools
How agents can reason, make decisions, and interact with external tools or data to complete tasks
The way AWS approaches agent development through three main paths: ready-to-use tools, managed services for building agents, and fully custom solutions for more advanced development
What AI agents are, how they work, and where AWS services such as Amazon Q and Amazon Bedrock fit into the wider agent ecosystem.

Curriculum

9 modules

Module 1: From LLMs to Agents
Module 2: Exploring Agentic AI
Module 3: Understanding Agentic AI Workflows
Module 4: Introducing Autonomous Agents
Lab: Accelerating Software Development using Amazon Q Developer
Module 5: Amazon Q and Agentic Development Tools
Lab: Building Your First AI Agent with Strands Agents SDK
Module 6: Agentic AI with Amazon Bedrock
Module 7: Building DIY Solutions

Prerequisites

You should already be comfortable with the basic ideas behind generative AI and have some familiarity with AWS and software development.

This course assumes you understand concepts such as large language models, prompting, and how cloud services are typically used in applications.

Without that background, much of the discussion around agent architectures, tools, and orchestration will be too abstract.

You are therefore expected to have completed Generative AI Essentials or have equivalent hands-on experience working with generative AI systems.

You should also have foundational AWS knowledge and basic development experience, as the course references common AWS services and development workflows throughout.

Exam info

This course does not have an exam.

Course Dates

Sorry, there are currently no dates available for this course. Please submit an enquiry and one of our team will contact you about potential future dates or alternative options.

FAQs

4 Questions

Yes, we do provide courses suitable for beginners. However, Firebrand's accelerated courses aren't easy and it's essential that you are interested and actively pursuing a career in IT.

Traditional training providers usually run their courses from 9am to 5pm. At Firebrand Training we maximise the number of learning hours to minimise the number of training days, so you’ll be back to your job as quickly as possible. You don’t waste time travelling to several courses and finding an exam centre after that.

Firebrand's accelerated courses are constantly reviewed. We ask our delegates for feedback after every course. We are official partners with leading vendors and therefore, we're provided with certification changes and updates, which we can then implement in our course delivery at a very early stage. This feedback is then analysed in view of changes or discrepancies. We will then address the topics mentioned and have a panel of subject matter experts provide us with valuable suggestions for improvement and solutions.

If you need to learn new skills and you want to be able to put them into practice quickly, then Firebrand is the right training company for you.

Our unique accelerated training method means that we are your fastest way to learn. By delivering training for up to 12 hours per day, seven days per week, with exam centres on-site, we ensure that you are trained and certified quicker than anywhere else, having spent less time out of the office away from the day job.

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