Strands Agent Learning Path

  • IntermediateLevel

  • 133+Students Enrolled

  • 6 Hrs Duration

  • 4.7Average Rating

About this Course

  • Start from the fundamentals by building your first AI agent using Strands Agents, understanding agent structure, tools, prompts, and execution flow through hands-on examples.
  • Advance your skills by learning Model Context Protocol in depth, enabling structured context handling, better tool coordination, and more reliable, controllable agent behavior.
  • Design and implement multi agent systems where agents collaborate, delegate tasks, and communicate effectively to solve complex workflows using proven coordination patterns.
  • Deploy production-ready agents using Amazon Bedrock AgentCore, covering scalability, security, monitoring, and real-world deployment considerations for enterprise use cases.

Learning Outcomes

Build Your First Agent

Create a functional AI agent using Strands basics, tools, and prompts.

Master MCP Workflows

Apply Model Context Protocol to manage context, tools & agent state.

Design Multi Agent System

Build collaborative agents that coordinate, delegate, and solve tasks.

Deploy Agents at Scale

Deploy secure, scalable AI agents using Amazon Bedrock AgentCore.

Who Should Enroll

  • Data scientists, ML engineers, and developers who want to build, scale, and deploy real world AI agents.
  • Professionals exploring agent based AI, MCP, and multi agent systems for automation and complex workflows.
  • Product and AI leaders looking to understand production ready agent architectures using Amazon Bedrock.

Course Curriculum

Learn AI agents from fundamentals to deployment using Strands. Build agents, master MCP workflows, design multi agent systems, and deploy scalable, production ready agents with Amazon Bedrock.

tools

  1. 1. Introduction to Strands Agents

  2. 2. Working with Model Providers in Strands: Anthropic, LiteLLM, Ollama & Bedrock

  3. 3. Integrating AWS Tools with Strands Agents: S3 & DynamoDB in Action

  4. 4. Model Context Protocol (MCP) as Tools

  5. 5. Agent-to-Agent Communication

  6. 6. Observability with LangFuse and Evaluation with RAGAS

  1. 1. Getting Started with Agent AI: Setup & Foundations

  2. 2. Building Your First Agent: Architecture & Configuration

  3. 3. Exploring Model Providers: Configuration & Integration

  4. 4. Advanced Response Handling: Hooks & Event Processing

  5. 5. Empowering Agents with Tools: Creation & Execution

  6. 6. Managing Conversations: Session State & Persistence

  7. 7. Enhancing Agents with Memory: Storage & Retrieval

  1. 1. Course Introduction

  2. 2. Introduction to Multi-Agent Systems

  3. 3. Multi-Agent Systems with Swarm Intelligence

  4. 4. Multi-Agent Systems with Agent Graph

  5. 5. Multi-Agent System with a Agents as a Tools

  1. 1. Operating Agents in Production

  2. 2. Introduction to Amazon Bedrock AgentCore

  3. 3. Building agents with Amazon Bedrock AgentCore

Meet the instructor

Our instructor and mentors carry years of experience in data industry

company logo
Elizabeth Fuentes Leone

Developer Advocate

Elizabeth, AWS Developer Advocate, simplifies AI/ML and data concepts into practical guidance. Passionate about real-world solutions, she empowers developers via workshops, content, and community—focusing on GenAI, RAG, and developer tools.

company logo
Laura Salinas

Developer Advocate

Laura, Developer Advocate at AWS, helps engineers build scalable apps with serverless, AI/ML, and open-source tools. She simplifies cloud, shares insights, and empowers developer communities.

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Du’An Lightfoot

Sr. AI Engineer (AI/ML), AWS

Du'An Lightfoot is a Senior AI Engineer at AWS with 20+ years of experience. A frequent AWS re:Invent speaker and mentor, he helps teams build and scale agentic AI systems using Strands SDK and MCP integrations.

company logo
Shubham Londhe

Developer Advocate (AI/ML)

Shubham Londhe, Developer Advocate at AWS, helps developers adopt modern DevOps, Kubernetes, and cloud-native practices. With 8+ years in startups, he simplifies AWS concepts and mentors engineers to innovate and scale faster.

Get this Course Now

With this course you’ll get

  • 6 Hours

    Duration

  • Intermediate

    Level

Certificate of completion

Complete the course and pass the final assessment to receive an industry-recognized certificate

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

This learning path teaches you how to design, build, scale, and deploy AI agents using Strands Agents, Model Context Protocol, multi agent systems, and Amazon Bedrock, progressing from foundational concepts to production-ready implementations.

Model Context Protocol helps structure how agents manage context, tools, and memory. It improves reliability, control, and consistency of agent behavior, especially in complex workflows involving multiple tools or long-running tasks.

Structured context ensures agents receive the right information at the right time. It improves reliability, reduces hallucinations, and allows agents to operate consistently across long workflows, tool calls, and multi-agent interactions.

Tools allow agents to interact with external systems such as APIs, databases, or services. They extend agent capabilities beyond text generation, enabling real-world actions like data retrieval, computation, and task automation.

Common challenges include hallucinations, poor tool selection, context overload, and unpredictable behavior. Addressing these requires structured context, clear agent roles, controlled execution loops, and thoughtful system design.

Multi-agent systems distribute responsibilities across specialized agents. This allows parallel execution, clearer task ownership, and easier scaling compared to a single agent attempting to manage all logic and decisions.

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