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Eğitim İçeriği

Introduction

  • Differentiating between workflows and autonomous agents
  • Architectural overview and common use cases

Claude API Basics

  • Overview of the Messages API, available models, and key parameters
  • Crafting system prompts and defining structured outputs
  • Implementing streaming and robust error handling
  • Lab: Implementing the initial API integration

Tool Utilization

  • Specifying tools and their corresponding schemas
  • Understanding the tool-use loop and mechanisms for error recovery
  • Lab: Creating an agent that interacts with internal company APIs

MCP and Agent Skills

  • Role of MCP servers and clients in the ecosystem
  • Integrating existing MCP servers and developing a custom one
  • Leveraging Agent Skills to encapsulate reusable expertise
  • Lab: Connecting an agent to an external data source

Claude Agent SDK

  • Managing the agent loop, sessions, and context
  • Configuring permissions, hooks, and subagents
  • Lab: Developing a research and reporting agent

Cost, Quality, and Safety

  • Optimizing performance with prompt caching and token budgeting
  • Implementing guardrails and human approval workflows
  • Conducting evaluations using a curated test set
  • Setting up logging and continuous monitoring

Transitioning to Production

  • Deployment strategies and secure handling of secrets
  • Strategies for maintenance and version control
  • Developing a roadmap for participants' specific use cases

Kurs İçin Gerekli Önbilgiler

  • Programming proficiency in Python or TypeScript
  • Experience with REST API calls and JSON data handling
  • A foundational understanding of large language models

Target Audience

  • Software developers and data engineers integrating AI features into products and internal tools
  • Solution architects assessing agent-based architectures
  • Technical teams seeking to transition projects from prototype to production

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