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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)
Strong confirmation that current LLMs are deployable for customer projects when the proper setup is in place.
Oliver Fromm
Course - Agentic AI for Enterprise Applications
Machine Translated
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
The mix of theory and practice and of high level and low level perspectives
