AWS Agentic AI Foundations: Learn how to design and build autonomous AI agents in 2026.

Agentic AI represents the next evolution of AI: systems that can plan, act, reason and execute tasks independently.

This one-day training introduces the core principles and practical techniques for designing agentic AI solutions using AWS technologies such as Amazon Q, Kiro and Amazon Bedrock AgentCore, helping you apply these emerging capabilities in real scenarios.

  • Why get trained: Gain a foundational understanding of what makes AI “agentic”.
  • Why it matters: Skills in this area help you support innovation, solve complex tasks, and contribute to AI-driven automation initiatives using AWS services.
  • Who should attend: Software developers, technical professionals, AWS users and development teams exploring agentic AI

Enroll now in AWS Agentic AI Foundations and start applying autonomous agent principles using AWS services to real-world use cases with Trainocate – Global AWS Training Partner of the Year 2022-2025.

Overview

This one-day Agentic AI Foundations course guides you through building autonomous, goal-driven AI agents using AWS technologies.

What will you get out of this course:

  • Understand how Agentic AI differs from traditional conversational systems
  • Explore tools such as Amazon Q, Kiro, and Amazon Bedrock Agents/AgentCore
  • Learn design patterns, architectural strategies, and observability practices for agent systems

By the end of the course, you will be able to:

  • Distinguish between workflow, autonomous, and hybrid agents
  • Select suitable AWS services for agent development
  • Develop basic implementation patterns and interoperability strategies
  • Explain how to monitor, optimize, and scale agentic systems

Skills Covered

In this course, you will learn to:

  • Summarize the evolution of Agentic AI and define what makes something “agentic”
  • Identify core components of agentic systems
  • Distinguish between workflow, autonomous, and hybrid agents
  • Compare AWS service options for Agentic AI
  • Describe capabilities and use cases of Amazon Q Developer, Amazon Q Business, and Kiro
  • Explain Amazon Bedrock AgentCore and Amazon Bedrock Agents fundamentals
  • Identify basic implementation patterns for Agentic AI
  • Describe observability and interoperability patterns for production agentic AI systems

Prerequisites

We recommend that attendees of this course have:

Target Audience

This course is intended for:

  • Software developers new to Agentic AI seeking foundational knowledge
  • Technical professionals exploring AI capabilities and interested in core components and applications of agentic AI
  • Development teams evaluating Agentic AI solutions and needing to differentiate between agent types
  • AWS Users expanding into Agentic AI, including current users of Amazon Q Developer, Amazon Q Business, and Amazon Bedrock Agents

Course Curriculum

Module 1: From LLMs to Agents

  • Understanding Large Language Models (LLMs)
  • Innovations powering agents
  • Evolution timeline from LLMs to Agents

Module 2: Exploring Agentic AI

  • Understanding Agentic AI
  • Types of AI agents
  • Agentic AI applications

Module 3: Understanding Agentic AI Workflow

  • Workflow patterns
  • Amazon Bedrock flows overview

Module 4: Introducing Autonomous Agents

  • How Autonomous Agents work
  • ReAct
  • ReWoo
  • Multi-agent collaboration
  • AWS Agentic AI solutions

Module 5: Amazon Q and Agentic Development Tools

  • Amazon Q Developer
  • Amazon Q Business
  • Amazon Q in AWS Services
  • Kiro: AI-powered IDE with spec-driven development

Module 6: Agentic AI with Amazon Bedrock

  • Amazon Bedrock Agents
  • Amazon Bedrock AgentCore
  • Hands-on lab: Explore Amazon Bedrock Agents integrated with Amazon Bedrock Knowledge Bases and Amazon Bedrock Guardrails

Module 7: Building DIY Solutions

  • DIY solutions
  • Observability and Monitoring
  • Agent Interoperability

Module 8: Course Wrap-up

  • Next steps and additional resources
  • Course summary

Dates & Locations

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Speak to one of our learning experts today.

July 15, 2026 - July 15, 2026

Location: Kuala Lumpur
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July 15, 2026 - July 15, 2026

Location: Online
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August 5, 2026 - August 5, 2026

Location: Kuala Lumpur
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August 5, 2026 - August 5, 2026

Location: Online
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September 9, 2026 - September 9, 2026

Location: Kuala Lumpur
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Availability: TBC
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September 9, 2026 - September 9, 2026

Location: Online
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PROMO

October 7, 2026 - October 7, 2026

Location: Kuala Lumpur
Modal: ILT
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October 7, 2026 - October 7, 2026

Location: Online
Modal: VILT
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November 18, 2026 - November 18, 2026

Location: Kuala Lumpur
Modal: ILT
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November 18, 2026 - November 18, 2026

Location: Online
Modal: VILT
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December 9, 2026 - December 9, 2026

Location: Kuala Lumpur
Modal: ILT
Availability: TBC

December 9, 2026 - December 9, 2026

Location: Online
Modal: VILT
Availability: TBC
Trainocate exam and cert

Exam & Certification

There is no exam directly associated with this course. However, AWS offers an extensive portfolio of industry-recognized certifications that can help you stand out as a tech professional in 2026 and beyond. Achieving AWS credentials is one of the most effective ways to validate your skills and accelerate your career.

With our expert-led training, you’ll be prepared to:

  • Master in-demand capabilities across Cloud, Data & AI, and Cybersecurity — areas driving global digital transformation.
  • Prove your expertise with a globally respected credential recognized by employers worldwide.
  • Advance your career by enhancing your credibility, increasing your earning potential, and opening doors to new opportunities.

Explore our top AWS Certifications for 2026 and start building the skills that matter today.

Training & Certification Guide

Frequently Asked Questions

Agentic AI marks the evolution from reactive assistants to proactive, autonomous systems that can understand, decide, and act with minimal oversight.

AI agents access tools, data, and the internet to navigate complex tasks, adapt to changing conditions, and collaborate with other agents to get work done.

Unlike traditional AI that simply answers questions, agents can think, plan, remember, and learn from experience — driving efficiency and innovation across industries like finance, healthcare, retail, and customer service.

As transformative as the personal computer or the internet, AI agents are set to redefine how we work and live.

There are several business benefits to using agentic AI.

Increased efficiency

Agentic artificial intelligence enables businesses to simplify the complexity of various challenging or specialized tasks through automation. Instead of relying on human-driven manual practices, using agentic AI can automate tedious processes, freeing up time for your employees. Your employees can use the extra time that agentic AI saves them on more demanding tasks, such as problem-solving, strategic planning, and other drivers of growth.

Increased user trust

Agentic AI can offer a higher degree of personalization when interacting with customers. By utilizing existing customer data, agentic AI can quickly produce tailored messaging, engage with the customer in their preferred tone, and offer practical product recommendations. Over time, agentic AI improves customer relationships and builds trust between customers and your business.

Businesses can also utilize agentic artificial intelligence to analyze customer feedback, identify the most frequently occurring information, and provide it to product engineers. It can also directly respond to users who leave feedback, creating positive feedback loops where customers feel that their feedback is taken seriously by your company.

Continuous improvement

Agentic AI can continuously learn and improve, adapting to any tasks assigned to it. It interacts, learns from feedback, and optimizes its decision-making based on this recursive loop. For businesses, this means that it continues to deliver its benefits at higher and higher levels over time.

Human augmentation

Agentic AI can serve as a fantastic collaboration tool for human agents, enhancing their productivity and reducing the number of laborious manual tasks they must complete. By working alongside agentic AI models, human agents can overcome complex challenges, automate difficult decision-making pathways, and drive their efficiency.

Agentic AI can be single or multi-agent setups. In a single-agentic AI system, one AI agent handles all tasks sequentially. These are preferable when businesses need a faster solution that can work on a well-defined problem or process.

Multi-agentic AI, on the other hand, involves multiple AI agents collaborating to break down complex workflows into smaller segments. This approach is more scalable than single systems and is much more flexible for solving complex scenarios. The vast majority of agentic AI agents refer to this latter, more diverse form of AI deployment.

Here are a few different structures of multi-agent systems.

Horizontal multi-agent

Horizontal multi-agent AI is a system of working where every AI agent has the same level of technical proficiency and complexity. Each agent specializes in a narrow skill, bringing their findings together to solve a complex problem. This structure utilizes lateral collaboration and communication among the specialized AI agents.

Vertical multi-agent

In a vertical multi-agent system, there is a hierarchical structure in which lower-level AI agents have ‘easier’ tasks compared to the higher ones. The highest levels of this structure handle tasks that require more processing power and LLMs, such as critical thinking, reasoning, and decision-making. Lower-level AI agents in this structure perform tasks such as collecting data, formatting it, or processing it to pass it to higher levels.

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