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Duration 14 hours
Course Outline
Introduction
- Distinguishing workflows from agents
- High-level architecture and common use cases
Core Claude API Concepts
- Messages API, available models, and key parameters
- Crafting system prompts and managing structured output
- Implementing streaming and robust error handling
- Lab: Initial API integration
Implementing Tool Use
- Creating tool definitions and schemas
- Managing the tool-use loop and error recovery strategies
- Lab: Developing an agent that interacts with internal APIs
MCP and Agent Skills
- Understanding MCP servers and clients
- Integrating existing servers and developing custom ones
- Applying Agent Skills for reusable expertise
- Lab: Connecting an agent to a data source
Claude Agent SDK
- Managing the agent loop, sessions, and context
- Configuring permissions, hooks, and subagents
- Lab: Building a research and reporting agent
Cost, Quality, and Safety
- Utilizing prompt caching and managing token budgets
- Establishing guardrails and human approval mechanisms
- Conducting evaluations with a limited test set
- Implementing logging and monitoring solutions
Moving from Prototype to Production
- Exploring deployment options and secret management
- Strategies for maintenance and version control
- Developing a roadmap for participants' specific use cases
Requirements
- Familiarity with Python or TypeScript programming
- Experience with REST API calls and JSON processing
- Fundamental knowledge of large language models
Target Audience
- Software developers and data engineers integrating AI capabilities into products and internal tools
- Solution architects assessing agent-based architectures
- Technical teams aiming to transition projects from prototype stage to production
Testimonials (3)
The trainer is patient and very helpful. He knows the topic well.
CLIFFORD TABARES - Universal Leaf Philippines, Inc.
Course - Agentic AI for Business Automation: Use Cases & Integration
Good mixvof knowledge and practice
Ion Mironescu - Facultatea S.A.I.A.P.M.
Course - Agentic AI for Enterprise Applications
The mix of theory and practice and of high level and low level perspectives
