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Duration 14 hours
Course Outline
Introduction
- Workflows versus agents
- Architectural overview and common use cases
Claude API Fundamentals
- Messages API, models, and parameters
- System prompts and structured output
- Streaming and error handling
- Lab: Initial API integration
Tool Usage
- Defining tools and schemas
- The tool-use loop and error recovery
- Lab: An agent that interacts with company APIs
MCP and Agent Skills
- MCP servers and clients
- Utilising existing servers and developing a simple one
- Agent Skills for reusable expertise
- Lab: Connecting an agent to a data source
Claude Agent SDK
- Agent loops, sessions, and context management
- Permissions, hooks, and subagents
- Lab: Building a research and reporting agent
Cost, Quality, and Safety
- Prompt caching and token budgeting
- Guardrails and human approval steps
- Evaluations using a small test set
- Logging and monitoring
From Prototype to Production
- Deployment options and secrets management
- Maintenance and versioning
- Roadmap for participants' specific use cases
Requirements
- Programming experience in Python or TypeScript
- Familiarity with calling REST APIs and handling JSON
- A foundational understanding of large language models
Audience
- Software developers and data engineers integrating AI features into products and internal tools
- Solution architects assessing agent architectures
- Technical teams aiming to transition 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
