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Building Agentic AI with Amazon Bedrock AgentCore
Course 1930
- Duration: 1 day
- Language: English
- Level: Advanced
In this course, you'll learn how to advance your proof-of-concept agents to production-ready agentic AI solutions on AWS. You will use Amazon Bedrock AgentCore services for tool orchestration, identity management, and production monitoring to implement secure, scalable enterprise AI systems ready for deployment.
Build Agentic AI with Amazon Bedrock Course Delivery Methods
In-Person
Online
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Build Agentic AI with Amazon Bedrock Course Information
Course Benefits
- Define agentic AI characteristics and differentiate them from traditional AI systems.
- Identify the core agent components and their interactions.
- Describe how Bedrock AgentCore services support agentic AI.
- Deploy agents by using supported frameworks with AgentCore Runtime.
- Describe the core features of AgentCore Runtime.
- Configure serverless execution with session isolation.
- Configure AgentCore Identity for enterprise security requirements.
- Create policies to secure agent tool calls using AgentCore Policy.
- Implement secure token management and permission delegation.
- Ensure compliance with data governance and audit requirements.
- Implement different tool integration patterns, including built-in tools and protocol-based tools.
- Design and deploy Model Context Protocol (MCP) servers and clients for extensible agent capabilities.
- Describe common authentication patterns for agent tool use.
- Configure AgentCore Gateway components for secure and authorized tool access.
- Implement agentic memory patterns for different use cases.
- Configure AgentCore Memory operations for context-aware development.
- Optimize memory performance for production workloads.
- Configure AgentCore Observability for production monitoring.
- Implement Amazon CloudWatch integration and specialized tracing.
- Describe the core features of AgentCore Evaluations.
- Integrate agentic systems with production APIs and services.
- Design deployment strategies for production environments.
- Assess production readiness and establish continuous improvement processes
Prerequisites
We recommend that attendees of this course have:
Build Agentic AI with Amazon Bedrock Course Outline
Module 1: Introductions
- Foundations of Agentic AI Patterns
- Agent building blocks
- Amazon Bedrock AgentCore introduction
Module 2: AgentCore Runtime and Framework Integration
- Supported frameworks and implementation
- AgentCore Runtime overview
- Infrastructure and deployment
Module 3: Security and Identity Management
- Security and identity management
- Securing your agents with AgentCore Identity
Module 4: Tool Integration and AgentCore Gateway
- Amazon Bedrock AgentCore Policy
- Built-in tools and custom integration
- Model Context Protocol (MCP)
- AgentCore Gateway
- Implementing AgentCore Gateway
- Amazon Bedrock AgentCore Policy
Module 5: Agentic Memory Implementation
- Agentic memory core concepts
- AgentCore Memory
- Securing AgentCore Memory
- Hands-on Lab: Enhance and Scale Agents with Amazon Bedrock AgentCore
Module 6: Production Monitoring and Observability
- Monitoring agents with AgentCore Observability
- Verifying agent performance with AgentCore Evaluation
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Build Agentic AI with Amazon Bedrock Course FAQs
- Software developers seeking intermediate knowledge for building agentic systems
- Technical professionals exploring AI capabilities and interested in building agentic AI systems.
- Development teams building agentic AI solutions
An AI agent is an LLM-powered decision maker that can think through tasks and do something in the real world (not just generate text).
Amazon Bedrock AgentCore is a foundational orchestration and runtime layer for AI agents within Amazon Bedrock. It provides the core services needed to build, run, connect, and manage autonomous or semi-autonomous AI agents that use large language models (LLMs), tools, and enterprise data.