719-298-4047info@force7training.com
Student Login
Force7 Training
FRCAWS-16AWS

Agentic AI Foundations

Duration · 1 dayVirtual + In-PersonInstructor-Led

$650

Course Description

This 1-day instructor-led course introduces students to the foundational concepts, architectures, tools, and operational models behind Agentic AI systems. Participants learn how autonomous and semi-autonomous AI agents operate, how they reason and interact with tools and data, and how organizations are beginning to deploy agent-based AI systems in real-world environments.

The course combines lectures, demonstrations, architecture discussions, and guided hands-on activities to provide a practical understanding of Agentic AI technologies, workflows, governance, and enterprise applications.

— Be First in Line —

Register Your Interest

We're finalizing the schedule for Agentic AI Foundations. Add your details below and we'll notify you the moment a session opens for registration — no payment or commitment required.

Audience Profile

This course is intended for:

  • Software Developers
  • Cloud Engineers
  • AI/ML Engineers
  • Data Scientists
  • Solutions Architects
  • Technical Managers
  • Innovation Teams
  • Business Technology Leaders

Prerequisites

Before enrolling, you should have:

  • Basic understanding of artificial intelligence concepts
  • Familiarity with APIs and cloud computing concepts
  • General knowledge of automation and software applications helpful
  • No advanced machine learning experience required

— What You'll Learn —

Learning Objectives

In this course, you will learn to:

  • 1Understand the fundamentals of Agentic AI systems
  • 2Differentiate between traditional AI and AI agents
  • 3Explain how AI agents reason, plan, and execute tasks
  • 4Identify key components of agent architectures
  • 5Understand tool use, memory, and orchestration concepts
  • 6Explore common enterprise use cases for Agentic AI
  • 7Discuss governance, security, and ethical considerations
  • 8Design basic agent-driven workflows

— Day-by-Day —

Course Outline

Module 1

Introduction to Agentic AI

Topics

  • What is Agentic AI?
  • Evolution from chatbots to intelligent agents
  • Autonomous vs assistive AI systems
  • AI reasoning and decision-making concepts
  • Enterprise adoption trends
  • Common Agentic AI terminology
  • Discussion Topics
  • Current industry use cases
  • Benefits and limitations of AI agents
  • Risks associated with autonomous systems
  • Demonstration
  • Example AI agent workflows
  • Agent-based task execution examples

Module 2

Core Components of AI Agents

Topics

  • Agent architecture fundamentals
  • Planning and reasoning engines
  • Tool use and API integration
  • Memory systems and context retention
  • Multi-step task execution
  • Agent communication patterns
  • Concepts Covered
  • Retrieval-Augmented Generation (RAG)
  • Function calling
  • Prompt chaining
  • Reflection and self-correction
  • Multi-agent collaboration
  • Hands-On Exercise
  • Build a simple agent workflow
  • Configure tool-calling logic
  • Simulate agent decision-making

Module 3

Large Language Models and Agentic AI

Topics

  • Role of Large Language Models (LLMs)
  • Foundation models and inference
  • Prompt engineering fundamentals
  • Structured output generation
  • Hallucination reduction techniques
  • Context window management
  • Demonstration
  • Comparing prompt strategies
  • Building conversational reasoning flows
  • Structured response generation

Module 4

Agentic AI Frameworks and Ecosystems

Topics

  • Overview of modern AI agent frameworks
  • Workflow orchestration concepts
  • Event-driven AI systems
  • AI agents in cloud environments
  • Integration with enterprise systems
  • Observability and monitoring concepts
  • Technologies Discussed
  • LangChain
  • LangGraph
  • CrewAI
  • AutoGen
  • Vector databases
  • API orchestration platforms
  • Hands-On Exercise
  • Design a multi-step agent process
  • Explore orchestration examples
  • Connect agents to external data sources

Module 5

Enterprise Use Cases for Agentic AI

Topics

  • IT operations automation
  • Customer service agents
  • AI research assistants
  • Workflow automation
  • Security operations assistants
  • Knowledge management systems
  • Software engineering assistants
  • Group Discussion
  • Industry-specific AI opportunities
  • Operational efficiency improvements
  • Human-in-the-loop workflows

Module 6

Governance, Security, and Responsible AI

Topics

  • Responsible AI principles
  • Security risks and safeguards
  • AI alignment considerations
  • Data privacy and compliance
  • Human oversight models
  • Ethical and operational governance
  • Discussion Topics
  • Risks of autonomous actions
  • Governance frameworks
  • Monitoring AI behavior in production

Module 7

Building an Agentic AI Strategy

Topics

  • Evaluating business readiness
  • Identifying automation opportunities
  • Scaling AI agents responsibly
  • Measuring business value
  • Adoption and operational challenges
  • Future trends in autonomous AI
  • Workshop Activity
  • Design a basic enterprise AI agent solution
  • Present use case and architecture concepts
  • Discuss governance and deployment considerations

— Where to Next —

Recommended Certifications

Industry-recognized credentials that build on what this course covers.

  • and Learning Paths
  • AI and Machine Learning Fundamentals
  • Cloud AI Engineering
  • Generative AI Application Development
  • Responsible AI and Governance

— Additional Details —

What else is included

Workshop Activity

Design a basic enterprise AI agent solution Present use case and architecture concepts Discuss governance and deployment considerations

Hands-On Activities Included

  • Students complete guided exercises covering:
  • Agent workflow design
  • Prompt engineering
  • Tool-calling workflows
  • AI reasoning demonstrations
  • Multi-step task orchestration
  • Retrieval-Augmented Generation concepts
  • Multi-agent coordination examples
  • Governance and risk analysis

Suggested Course Materials

  • Student guide
  • Instructor presentation slides
  • AI architecture diagrams
  • Agent workflow examples
  • Hands-on exercise manual
  • Use case planning templates

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.

— Keep Exploring —

Need a different angle?

Browse the full AWS catalog or chat with an advisor about a custom training plan for your team.