Every AI system is still just a program underneath. We make sure that foundation is solid before touching a single model.

• Python fundamentals, data structures & OOP for AI systems
• NumPy & Pandas for data handling
• APIs, JSON & system integration
• File handling, automation scripts & modular code
• Debugging, logging & configuration workflows

Before you can build an agent, you need to understand what you're actually talking to, and how to talk to it well.

• Transformers, attention & tokenization
• How LLMs generate text: context windows & next-token prediction
• Prompt engineering: zero-shot, few-shot, chain-of-thought
• System prompts, role based prompting & structured output
• Function calling and structured JSON responses
• Comparing model choices: GPT, Claude, Gemini, Llama & Granite

An LLM only knows what it was trained on. This module is about connecting it to your own, current data, accurately.

• Generating and comparing embeddings
• Chunking strategies & data ingestion
• Vector databases: FAISS, ChromaDB & Pinecone
• Retrieval pipelines & semantic search
• Reranking, hybrid retrieval & reducing hallucinations
• Agentic RAG: retrieval inside a reasoning loop

This is where the program turns a corner, from calling a model to building something that reasons and acts on its own.

• What makes a system "agentic" versus a simple chatbot
• Tool calling & chaining with LangChain Expression Language (LCEL)
• Extracting tool inputs from LLM outputs, then validating and executing the call
• ReAct style reasoning: think → act → observe
• Using built-in LangChain agents to analyze data, generate visualizations & run database queries
• Short-term & long-term memory for agents
• Designing and binding custom tools safely: accuracy, safety & cost tradeoffs

Single agents hit a ceiling fast. LangGraph is how you give a system memory, structure, and the ability to correct itself as it works.

• Workflows built as graphs: state, nodes & edges
• Conditional logic & iteration in agent workflows
• Agents that improve themselves: Reflection, Reflexion & ReAct architectures
• Structuring agent feedback and integrating external data while reasoning
• Human-in-the-loop checkpoints for higher-stakes decisions
• Agentic RAG systems that route queries and support reasoning enhanced by retrieval
• Agent orchestration, query routing & governance for reliability at scale

One agent can only do so much. This module is about splitting work across a team of agents the way you'd delegate across a real team.

• Structuring agents, tasks & tools into crews
• Designing agent roles and delegating tasks
• Selecting and combining agentic frameworks & architectural patterns for a given problem
• Memory management across multiple collaborating agents
• Agents driven by conversation, built with AG2 (AutoGen) and BeeAI
• Agent-to-agent communication patterns
• Comparing frameworks (LangGraph vs. CrewAI vs. AG2 vs. BeeAI) for real-world fit

MCP is quickly becoming the standard way agents connect to tools and data. This module doubles as direct prep for Anthropic's own architecture exam.

• Model Context Protocol: servers, clients, tools, resources & prompts
• Connecting models, tools and data through MCP
• Claude Agent SDK & Claude Code workflows
• Structured output design and context window management
• Reliability patterns: multi-instance review and coherence across long conversations
• Mapped to Anthropic's Claude Certified Architect – Foundations exam domains

Most companies hiring for agentic AI roles already run on Azure. This module makes sure you can ship there too.

• Azure AI Foundry: projects, model deployment & managed identity
• Implementing generative AI & agentic solutions on Azure
• Grounding agents with Azure AI Search & RAG patterns
• Responsible AI, content filtering & security on Azure
• Practice aligned to the exam: planning & managing Azure AI solutions

A working notebook isn't a product. This is the module where your agent becomes something other people can actually rely on.

• Serving agents with FastAPI & containerizing with Docker
• Deploying to the cloud & designing for scale
• Prompt versioning, tracing & observability
• Evaluation pipelines, guardrails & hallucination checks
• Caching, batching & cost/latency optimization
• CI/CD for AI applications

• End-to-end capstone: build & ship a multi-agent product
• Mock interviews with AI engineering hiring panels
• Sample exam papers for CCA-F and AI-103
• Resume & LinkedIn profile built around your project portfolio