Written by the AgileFever editorial team. Program details were checked against the live program pages in October 2026. We run this program, so this is a vendor walkthrough of its published curriculum.
Curriculum pages tend to be a list of tool names. This one goes module by module and says what you can do after each one, so you can check it against what you already know and decide where you would be reviewing and where you would be learning.
All 18 modules
| # | Module | What it covers |
|---|---|---|
| 1 | Overview of GenAI and the rise of agentic AI | How LLMs work and fail, and how an agent differs from a single prompt and answer |
| 2 | Prompt and context engineering | Few-shot and chain-of-thought prompts; managing the context window by compression, pruning, routing and budgeting |
| 3 | Advanced RAG engineering | Embeddings, chunking, vector databases (FAISS, Pinecone, Weaviate), hybrid search, query rewriting |
| 4 | Fine-tuning, multimodal AI and document intelligence | LoRA and QLoRA, when to fine-tune instead of using RAG, OCR, PDF and invoice extraction |
| 5 | Agentic AI foundations | The planner, executor, tools and memory that make up an agent |
| 6 | Agent architectures | ReAct, Plan-and-Execute, Tree of Thought, goal decomposition |
| 7 | Agent frameworks | CrewAI, AutoGen and LangGraph, and how to choose between them |
| 8 | Agentic RAG | Retrieval inside agent loops, evaluated with RAGAS and DeepEval |
| 9 | Tool use and structured outputs | Function calling, JSON mode, Pydantic validation, retry strategies |
| 10 | Memory in agents | Short-term, long-term and vector memory, persisted in LangGraph |
| 11 | Planning and reasoning | Multi-step planning, dynamic tool chaining, decisions under constraints |
| 12 | Multi-agent collaboration | Role-based orchestration, negotiation and message passing between agents |
| 13 | Agent deployment and infrastructure | Docker, Hugging Face Spaces, AWS Lambda, GCP, LLMOps basics, evaluating agent responses |
| 14 | Enterprise AI agents on Azure | Agents in Microsoft Foundry and enterprise RAG with Azure AI Search, toward a Microsoft Learn badge |
| 15 | Security, alignment and safety | Loop prevention, permission boundaries, practical prompt-injection defenses |
| 16 | Agent-as-a-Service | Packaging an agent so a business can run or sell it as a service |
| 17 | Agent communication protocols | MCP, A2A and ACP, and how agents talk to tools and to each other |
| 18 | n8n (recorded module) | Workflow automation with n8n |
Hours listed on the program page
Additional discounts are available. Contact us to know more.
Where you would be reviewing, and where learning
Experienced engineers rarely need every module. The table below is a rough guide. It is our reading of the curriculum, not a promise about your experience.
| If you already | Expect mostly review | Expect mostly new |
|---|---|---|
| Build LLM apps with RAG | Modules 1 to 4 | Modules 5 to 12 (agent design) and 15 (security) |
| Run containers and cloud deployments | Module 13 | Modules 5 to 12, and agent evaluation |
| Build ML models but not agents | Module 1 and parts of 4 | Modules 2, 3 and 5 onward |
| Know Python but have not used LLM APIs | None | Most modules; plan extra time for modules 2 and 3 |
What the program does not cover
- Training models from scratch or classical machine learning. The AI and ML BootCamp handles that.
- Operating a large model-serving platform. That sits closer to the MLOps BootCamp.
- Deep, hands-on work on every cloud. Azure gets its own module, and module 13 touches AWS Lambda and GCP.
Capstone and assessment
There is no exam. The capstone is the final evaluation: two capstones are built live in class, and six more briefs are on GitHub for you to build alone. The page body lists 10+ hands-on projects across the modules (the page title says 14+, so ask which number is current). If you have a real use case at work, pick the capstone closest to it.
The program is built for experienced IT professionals who can read and write code. A Python for AI module comes with registration for people who need a refresher, and every live session is recorded. For the skills view of the same material, read the skills you will build. To compare it with the broader generative AI program, see Gen AI BootCamp vs GenAI and Agentic AI BootCamp. The full page is the GenAI and Agentic AI BootCamp.
Questions people ask
Is the order of modules fixed?
Yes. Each stage builds on the one before it, so retrieval comes before agents and deployment comes after both.
Do I need to know LangGraph or CrewAI beforehand?
No. Frameworks are taught in module 7 and used in the modules after it. You do need to be comfortable writing Python.
Are sessions recorded?
Yes. Every live session is recorded with lifetime access, so a missed class can be caught up.
