Module 1: Foundations – Differentiating Generative vs. Agentic AI and the core Perceive → Reason → Act → Reflect loop.
Module 2: Python & APIs – Mastering the “glue code,” REST APIs, and asynchronous programming.
Module 3: Prompt Engineering – Focusing on function calling, structured JSON outputs, and tool selection.
Module 4: RAG – Building the knowledge base using embeddings, vector databases, and memory systems.
Module 5: Agent Frameworks – Hands-on with LangGraph, CrewAI, and AutoGen for multi-agent orchestration.
Module 6: Automation – Integrating with low-code tools like n8n, Zapier, and Make.
Module 7: Vector Databases – Implementing long-term memory via Pinecone, Weaviate, and ChromaDB.
Module 8: Infrastructure – Selecting models and optimizing inference speeds with Groq and OpenAI.
Module 9: Observability – Tracing, debugging, and evaluating agent performance with LangSmith.
Module 10: Multi-Agent Systems – Designing role-based collaboration, debate patterns, and delegation.
Module 11: Design Patterns – Implementing ReAct, Tree-of-Thought, and self-healing architectures.
Module 12: Capstone – Building end-to-end autonomous research, support, and business systems.