Building AI agents with Amazon Bedrock AgentCore

  • BeginnerLevel

  • 111+Students Enrolled

  • 1 HrDuration

  • 4.8Average Rating

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About this Course

  • This course introduces Amazon Bedrock AgentCore and its core components, helping you understand how agent runtime, memory, and observability fit together to support real-world.
  • Learn how to deploy production-ready AI agents in minutes using AgentCore Runtime, with simple CLI commands and built-in session handling, monitoring, and scaling.
  • Explore adding long-term memory and deep observability, enabling agents to remember user context across sessions while tracking latency, tokens errors, and workflow out of the box.

Learning Outcomes

AgentCore Fundamentals

Understand AgentCore components, runtime, & memory.

Agent Deployment

Deploy scalable AI agents using AgentCore Runtime in minutes.

Memory & Observability

Add long-term memory and monitor agent behavior with built-in tools

Who Should Enroll

  • Developers and ML engineers who want to deploy production-ready AI agents quickly without managing Docker & Kubernetes.
  • AI practitioners building agent-based systems who need long-term memory, and personalization across user interactions.
  • Architects and technical teams looking to observe, debug, and monitor AI agents.

Course Curriculum

Learn vector database fundamentals, embeddings, and similarity search, then go hands-on with FAISS and Chroma to build a mini RAG MVP that lets you chat with your own documents.

tools

  1. 1. Introduction to Amazon Bedcork AgentCore

  2. 2. Bring AI agents into production in minutes with AgentCore Runtime

  3. 3. Bring AI agents with Long-Term memory into production in minutes

  4. 4. Observe your agent applications on Amazon Bedrock AgentCore Observability

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.

Get this Course Now

With this course you’ll get

  • 1 Hour

    Duration

  • Elizabeth Fuentes Leone

    Instructor

  • Beginner

    Level

Certificate of completion

Earn a professional certificate upon course completion

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

Frequently Asked Questions

Looking for answers to other questions?

Amazon Bedrock AgentCore is a managed framework for running AI agents in production. It simplifies deployment, session handling, memory, and observability, allowing teams to focus on agent logic rather than infrastructure or DevOps complexity.

AgentCore Runtime enables production deployment using simple CLI commands without Docker or Kubernetes. It automatically manages scaling, sessions, monitoring, and execution environments, making it easy to move AI agents from prototypes to reliable production systems.

Long-term memory allows agents to retain user preferences and insights across sessions. This transforms agents from stateless responders into context-aware systems capable of personalization, continuity, and more intelligent interactions over time.

AgentCore Memory provides structured, cross-session memory managed by the platform, unlike prompt-based memory which is limited to context windows. This ensures consistent recall, better personalization, and scalable memory handling without manual prompt engineering.

Observability helps teams understand how agents behave in real environments. AgentCore Observability provides built-in tracing, metrics, and workflow visualization, enabling faster debugging, performance tuning, and trust in agent-driven applications.

AgentCore Observability tracks session counts, latency, token usage, error rates, and execution flows. These insights help teams optimize performance, control costs, and identify failures early without additional monitoring configuration.

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