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Duration 14 hours
Course Outline
Introduction
- Workflows versus agents
- Architecture overview and typical use cases
Claude API fundamentals
- Messages API, models and parameters
- System prompts and structured output
- Streaming and error handling
- Lab: first API integration
Tool use
- Defining tools and schemas
- The tool-use loop and error recovery
- Lab: an agent that calls company APIs
MCP and Agent Skills
- MCP servers and clients
- Using existing servers and writing a small one
- Agent Skills for reusable expertise
- Lab: connect an agent to a data source
Claude Agent SDK
- Agent loop, sessions and context management
- Permissions, hooks and subagents
- Lab: build a research and reporting agent
Cost, quality and safety
- Prompt caching and token budgeting
- Guardrails and human approval steps
- Evaluations with a small test set
- Logging and monitoring
From prototype to production
- Deployment options and secrets handling
- Maintenance and versioning
- Roadmap for the participants’ own use case
Requirements
- Programming experience in Python or TypeScript
- Experience calling REST APIs and handling JSON
- Basic understanding of large language models
Audience
- Software developers and data engineers who build AI features into products and internal tools
- Solution architects evaluating agent architectures
- Technical teams that want to move from prototype 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
