A comprehensive, hands-on tutorial series teaching agentic AI from fundamentals to production systems.
Build single agents with tool-calling capabilities and learn the O.V.E. testing methodology.
- Building tool-calling agents from scratch
- Observe-Validate-Evaluate (O.V.E.) testing methodology
- Structuring system prompts for reliability
- Implementing the 7-step agent loop
- Read: Start with the Concepts & Guides to build your mental model.
- Lab: Complete the Lab Exercises to build the agent.
(Available - Refinements in progress)
Design coordinator-worker architectures with specialized agents and message protocols.
- Coordinator-worker orchestration patterns
- Designing specialized agents with distinct boundaries
- Implementing inter-agent message protocols
- Managing shared state across multiple agents
- Read: Explore the Multi-Agent Concepts.
- Lab: Build the system in Lab 2.
(In Development - Documentation and exercises available)
Enhance agents with long-term memory and retrieval-augmented generation using LlamaIndex.
- RAG pipeline architecture and implementation
- Vector databases and semantic search
- Multi-model orchestration (Llama for planning, DeepSeek for coding)
- Knowledge integration with agent systems
- Testing retrieval quality with O.V.E. methodology
- Read: Start with the Memory & RAG Concepts.
- Lab: Build RAG-powered agents in Lab 3.
- ✅ Core documentation complete (RAG architecture, embeddings, multi-model coordination)
- ✅ Lab exercises defined (4 exercises: LlamaIndex setup → Project embedding → Multi-model → Architect-Builder)
- 🚧 Code scaffolds in place, awaiting implementation
- 🚧 O.V.E. test harness for RAG systems in development
Cross-tutorial reference materials to support your learning:
- Glossary - Comprehensive definitions of all key terms
- Software Patterns - Design patterns and SOLID principles demonstrated
- Tech Stack - Technology decisions and rationale
Lesson 4: Production Patterns Monitoring, scaling, deployment, and reliability engineering.
Lesson 5: Advanced Frameworks Integration with LangChain, CrewAI, and other ecosystem tools.