Written by the AgileFever editorial team. Program details were checked against the live program pages in October 2026. We teach this program, so the list below comes straight from its published curriculum.
“Agentic AI” is a loose term, so it helps to say what you can do after finishing a course instead of what it covered. This post goes through the skills the GenAI and Agentic AI BootCamp is built around, in the order you build them, and how each one shows up at work.
The skill map
| Skill | What you can do afterward | Where it appears at work |
|---|---|---|
| Prompt and context engineering | Manage a context window with compression, pruning and routing | Keeping long-running assistants accurate and affordable |
| Advanced RAG | Choose chunking, embeddings and hybrid search; rewrite queries | Search over policy, support and engineering documents |
| Agent architectures | Pick between ReAct, Plan-and-Execute and Tree of Thought | Deciding how much freedom an agent gets |
| Frameworks | Build in LangGraph, CrewAI and AutoGen and choose between them | Avoiding a rewrite when requirements change |
| Tool use and structured outputs | Use function calling, JSON mode and Pydantic validation with retries | Connecting an agent to ticketing, CRM or internal APIs |
| Memory | Combine short-term, long-term and vector memory; persist it in LangGraph | Assistants that remember a customer or a case |
| Planning | Break goals into steps and chain tools dynamically | Multi-step work such as report generation or triage |
| Multi-agent collaboration | Coordinate role-based agents and pass messages between them | Splitting research, drafting and review across agents |
| Evaluation | Measure accuracy and hallucination with RAGAS and DeepEval | Proving an agent is good enough to release |
| Security and safety | Prevent loops, set permission limits, defend against prompt injection | Security review before launch |
| Deployment | Ship with Docker, Hugging Face Spaces, AWS Lambda or GCP | Running outside a laptop |
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Count of modules per stage, from the program page
The skill that matters most is evaluation
Anyone can get an agent to work once. The difficult part is knowing it keeps working: that retrieval still finds the right passage, that a tool call fails safely, that a new model version did not change behaviour. The curriculum places evaluation inside the agentic RAG module and again in deployment, which is the right place for it. When you look at any agent course, check whether evaluation is taught or only demonstrated.
What you build
The program lists 10+ projects and eight capstone options. Two capstones are built live in class and the other six come as briefs on GitHub for you to build on your own. The capstone is the final evaluation; there is no exam. Because projects are the proof of skill here, ask to see the capstone options before you enrol and pick one that resembles a problem at your own company.
What you need before you start
The program is for experienced IT professionals. You should read and write code comfortably, because the sessions move into LangGraph and CrewAI quickly. A recorded Python for AI module comes with registration for people who need a refresher. The cost is $2,000 in the US over 74 hours (64+ live), and every live session is recorded.
Skills it does not cover
- Training models from scratch or classical machine learning. The AI and ML BootCamp handles that.
- Running large model-serving platforms, which sits closer to the MLOps BootCamp.
- Deep experience on every cloud. Azure gets a dedicated module; for AWS and Google Cloud, see the 12-hour Enterprise AI Agents courses.
To decide between this program and the broader generative AI course, read Gen AI BootCamp vs GenAI and Agentic AI BootCamp. For the full program page, see the GenAI and Agentic AI BootCamp.
