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 Duration 14 hours

Course Outline

Introduction

  • Distinguishing between workflows and autonomous agents
  • Architectural overview and common application scenarios

Claude API Essentials

  • The Messages API, model selection, and configuration parameters
  • System prompt design and structured data output
  • Implementing streaming responses and robust error management
  • Lab: Initial API integration exercise

Tool Utilization

  • Defining tool specifications and data schemas
  • The tool-use loop mechanism and error recovery strategies
  • Lab: Developing an agent that interacts with internal company APIs

MCP and Agent Skills

  • Understanding MCP servers and client interactions
  • Leveraging existing MCP servers and developing a basic custom server
  • Implementing Agent Skills for reusable domain expertise
  • Lab: Linking an agent to an external data source

Claude Agent SDK

  • Agent execution loops, session management, and context handling
  • Configuring permissions, hooks, and subagents
  • Lab: Creating a research and reporting agent

Cost, Quality, and Security

  • Implementing prompt caching and managing token budgets
  • Establishing guardrails and human-in-the-loop approval processes
  • Conducting evaluations with a small test dataset
  • Implementing comprehensive logging and monitoring

Transitioning to Production

  • Deployment strategies and secure secret management
  • Ongoing maintenance and version control practices
  • Developing a roadmap for your specific use case

Requirements

  • Familiarity with programming in Python or TypeScript
  • Practical experience with REST API calls and JSON processing
  • Fundamental knowledge of large language models

Target Audience

  • Software engineers and data specialists integrating AI capabilities into products and internal solutions
  • Solution architects assessing agent-based architectures
  • Technical teams aiming to transition AI solutions from prototype to production environments

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