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Building Agentic AI with Amazon Bedrock

Duration · 1 dayVirtual + In-PersonInstructor-Led

$650

Course Description

This one-day instructor-led course introduces participants to building agentic AI solutions using Amazon Bedrock on Amazon Web Services. Learners explore how AI agents reason, plan, retrieve information, invoke tools, and automate workflows using foundation models, knowledge bases, and orchestration capabilities available in Amazon Bedrock.

The course combines foundational concepts, architecture guidance, demonstrations, and hands-on labs focused on designing intelligent AI agents for enterprise use cases.

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Audience Profile

This course is intended for:

  • Developers
  • Solutions architects
  • AI engineers
  • Cloud engineers
  • Technical managers
  • Innovation teams

Prerequisites

Before enrolling, you should have:

  • Basic AWS Cloud knowledge
  • Familiarity with APIs and cloud applications
  • General understanding of generative AI concepts recommended

— What You'll Learn —

Learning Objectives

In this course, you will learn to:

  • 1Define agentic AI concepts and architectures
  • 2Explain how AI agents reason and perform tasks
  • 3Describe Amazon Bedrock capabilities for agentic AI
  • 4Build and configure AI agents using Amazon Bedrock
  • 5Integrate foundation models with tools and APIs
  • 6Implement Retrieval-Augmented Generation (RAG)
  • 7Use knowledge bases to improve contextual responses
  • 8Design secure and responsible AI agent workflows
  • 9Identify enterprise use cases for autonomous AI systems

— Day-by-Day —

Course Outline

Module 1

Introduction to Agentic AI

Topics

  • What is agentic AI?
  • Evolution from chatbots to autonomous agents
  • Characteristics of AI agents
  • Planning, reasoning, and orchestration concepts
  • Human-in-the-loop workflows
  • Enterprise use cases for AI agents
  • Demonstration
  • Exploring real-world agentic AI scenarios

Module 2

Generative AI and Foundation Models

Topics

  • Review of generative AI fundamentals
  • Understanding foundation models and LLMs
  • Prompt engineering for agents
  • Model context and memory
  • Tool use and function calling concepts
  • Limitations and challenges of AI agents
  • Hands-On Exercise
  • Prompt engineering for autonomous task execution

Module 3

Amazon Bedrock Overview

Topics

  • Introduction to Amazon Bedrock
  • Supported foundation model providers
  • Bedrock architecture and components
  • Model access and customization options
  • Security, governance, and compliance
  • Cost optimization considerations
  • Demonstration
  • Navigating Amazon Bedrock services and features

Module 4

Building AI Agents with Amazon Bedrock

Topics

  • Introduction to Amazon Bedrock Agents
  • Agent workflows and orchestration
  • Configuring instructions and objectives
  • Connecting agents to APIs and AWS services
  • Action groups and tool integration
  • Multi-step reasoning and task execution

Hands-On Lab

  • Creating and testing an AI agent in Amazon Bedrock

Module 5

Retrieval-Augmented Generation (RAG)

Topics

  • Understanding Retrieval-Augmented Generation
  • Knowledge bases and embeddings
  • Data ingestion and indexing
  • Improving response accuracy with contextual retrieval
  • Vector search concepts
  • Enterprise knowledge management use cases

Hands-On Lab

  • Building a knowledge-enabled AI agent

Module 6

Integrating Agentic AI into Applications

Topics

  • Integrating Bedrock agents with applications
  • API-driven architectures
  • Event-driven workflows
  • Serverless integration patterns
  • Monitoring and observability
  • Scaling AI agent solutions
  • Demonstration
  • End-to-end AI agent workflow example

Module 7

Responsible AI and Security

Topics

  • Responsible AI principles
  • Guardrails and content filtering
  • Data privacy and protection
  • Access control and identity management
  • Human oversight and approval workflows
  • Risk mitigation strategies
  • Group Discussion
  • Evaluating governance and ethical considerations

Module 8

Enterprise Use Cases and Best Practices

Topics

  • Customer support automation
  • IT operations assistants
  • Internal knowledge assistants
  • Workflow automation agents
  • Software development copilots
  • Best practices for production deployment
  • Activity
  • Designing an agentic AI solution for a business scenario
  • Course Wrap-Up
  • Review and Q&A
  • Key concept review
  • Amazon Bedrock services recap
  • Architecture best practices
  • Operational considerations
  • Final Q&A session
  • Suggested Hands-On Labs
  • Prompt engineering for AI agents
  • Creating an Amazon Bedrock Agent
  • Configuring action groups
  • Connecting agents to enterprise knowledge bases
  • Implementing RAG workflows
  • Building a task automation assistant

— Next Steps —

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Note: Course outlines are provided as a general guide. Content, pacing, labs, and instructional emphasis may vary based on instructor expertise, student experience levels, and customer-specific learning objectives.

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