A working demo is only the first step in building an AI application. You build a chatbot over a PDF, it answers three questions well, and the course ends before anyone asks how it would survive real users, real data, and a security review. The GenAI and Agentic AI BootCamp is built around the part that comes after the demo: agents that plan, use tools, remember context, work with other agents, and get deployed somewhere other than your laptop.
This guide covers what the program actually contains, who it’s built for, what it costs, and where it is the wrong choice. Everything below comes from the current program page, so you can check any of it against the bootcamp page yourself.
The program at a glance
| Total time | 74 hours over 8 weeks |
| Live, instructor-led | 64+ hours |
| Self-paced | 10 hours, including a Python for AI pre-work module you get on registration |
| Modules | 17 live modules plus a recorded n8n module |
| Projects | 10+ hands-on projects, 2 capstones built live, 6 more capstone briefs on GitHub |
| Evaluation | No exam. Your capstone is the final evaluation. |
| Recordings | Every live session is recorded, with lifetime access |
| Current price | $1,600 (US) or ₹70,000 (India), down from $2,000 and ₹85,000 |
Sixty-four live hours across eight weeks works out to roughly eight hours of live sessions a week, before project work. If you’re working full-time, plan your evenings or weekends around that number before you enroll, not after.
Who this bootcamp is built for
The program page is direct about this: it’s built for experienced IT professionals, not complete beginners. You don’t need prior AI experience, but you do need to be comfortable reading and writing code, because the sessions move into frameworks like LangGraph and CrewAI quickly.
The page lists the transitions the curriculum is designed around:
| If you’re currently a… | The bootcamp is aimed at moving you toward… |
|---|---|
| Software Engineer | Agentic AI Engineer |
| Backend Developer | LLM Engineer |
| AI/ML Engineer | Agentic AI Systems Engineer |
| Data Scientist | AI Automation Engineer |
| DevOps Engineer | AI Infrastructure Engineer |
| Full Stack Developer | AI Application Developer |
| Cloud Engineer | Agentic AI Developer |
If your starting point isn’t on that list and you’ve never written Python, start with Python for AI or The AI Blueprint first. Coming into this bootcamp without coding fluency means spending your live hours catching up instead of building.
What you’ll learn, in three phases
The 18 modules fall into three clear stretches: getting the GenAI foundations right, building agents, then making those agents production-ready.
Number of modules in each phase of the curriculum
Source: AgileFever GenAI and Agentic AI BootCamp curriculum, October 2026.
Eight of the 18 modules (about 44%) cover building agents. This phase includes memory, planning, and coordination between agents, alongside individual frameworks.
Phase 1: GenAI foundations
| Module | What it covers |
|---|---|
| Overview of GenAI and the rise of agentic AI | How LLMs work, where they fail, and how agentic systems differ from a single prompt-and-response |
| Prompt and context engineering | Few-shot and chain-of-thought prompting, plus managing context windows: compression, pruning, routing, and budgeting |
| Advanced RAG engineering | Embeddings, chunking, vector databases (FAISS, Pinecone, Weaviate), hybrid search, query rewriting |
| Fine-tuning, multimodal AI and document intelligence | LoRA and QLoRA, when to fine-tune instead of using RAG, OCR, PDF and invoice extraction |
Phase 2: Building agents
| Module | What it covers |
|---|---|
| Agentic AI foundations | The planner, executor, tools, and memory that make up an agent |
| Agent architectures | ReAct, Plan-and-Execute, Tree of Thought, goal decomposition |
| Agent frameworks | CrewAI, AutoGen, and LangGraph, and how to choose between them |
| Agentic RAG | Retrieval inside agent loops, plus evaluation with RAGAS and DeepEval for accuracy and hallucination |
| Tool use and structured outputs | Function calling, JSON mode, Pydantic validation, retry strategies |
| Memory in agents | Short-term, long-term, and vector memory, and persisting memory in LangGraph |
| Planning and reasoning | Multi-step planning, dynamic tool chaining, decisions under constraints |
| Multi-agent collaboration | Role-based orchestration, negotiation, and message passing between agents |
Phase 3: Production and enterprise
| Module | What it covers |
|---|---|
| Agent deployment and infrastructure | Docker, Hugging Face Spaces, AWS Lambda, GCP, LLMOps basics, evaluating agent responses |
| Enterprise AI agents on Azure | Building agents in Microsoft Foundry and enterprise RAG with Azure AI Search, toward a Microsoft Learn badge |
| Security, alignment and safety | Loop prevention, permission boundaries, and practical prompt-injection defenses |
| Agent-as-a-Service | Turning an agent into something a business can sell or run as a service |
| Agent communication protocols | MCP, A2A, and ACP, and when each one fits |
The n8n module is recorded rather than live. It covers building no-code workflows and connecting them to agents, which is useful if your team automates processes without writing every integration by hand.
The capstone projects
You build two capstones live during the program, chosen from a set of eight. The other six stay available as briefs on GitHub, so you can keep building after the cohort ends. Most of them are framed around a real business process rather than a toy example:
| Capstone | What you build |
|---|---|
| Regulatory LEA request automation | An agent that validates law-enforcement requests and routes sensitive actions through maker-checker approval, with an audit trail |
| Insurance claims processing | Extracts policy and incident details, checks coverage, flags fraud indicators, and proposes a settlement |
| IT service desk incident resolution | Classifies incidents, retrieves runbooks, suggests root causes, and runs approved fixes |
| Supply chain exception management | Spots shipment delays and shortages early and recommends rerouting with approval controls |
| Loan underwriting and portfolio monitoring | Runs KYC and credit-policy checks and produces explainable recommendations |
| Enterprise agentic system (HR copilot) | A supervisor agent coordinating policy answers and MCP-connected HR tools |
| Multi-agent product launch strategy | Collaborating agents that analyze market trends and position a new product |
| Go-to-market strategy with agentic AI | Agents that handle market research, competitor analysis, and planning for a launch |
Notice how many of them include an approval step. That’s the detail most hiring managers will ask about, because an agent that can freeze a bank account or approve a claim without a human checkpoint isn’t something any company will ship.
Career support after the sessions end
The program includes resume, LinkedIn, and GitHub reviews, mock interviews with feedback, one-to-one career mentoring, and exposure to hiring managers. It does not include a job guarantee. Hiring decisions remain with employers. What you walk away with is a portfolio of deployed work you can explain line by line in an interview.
When this bootcamp is the wrong choice
- You’ve never coded. Start with Python for AI first.
- You want to deploy AI inside client environments and handle stakeholder discovery, not just build agents. The AI Forward Deployed Engineer BootCamp is closer to that job.
- Your interest is mainly running models in production, pipelines, and monitoring. The MLOps, LLMOps and Agentic AI Ops BootCamp goes deeper on that side.
- You can’t commit around eight live hours a week for two months. Recordings help with a missed session, but falling behind on the agent modules compounds fast.
FAQ
Is there an exam?
No. Completing the capstone project is the final evaluation.
Do I need Python before I join?
You need basic programming comfort. The bootcamp includes a self-paced Python for AI module that you get access to once you register, and it’s meant to be finished before the live sessions start.
Can I do this while working full-time?
Yes, many learners do. Sessions are scheduled with working professionals in mind and every session is recorded. Plan for around eight live hours a week plus project time.
What happens if I miss a live session?
Every session is recorded and you keep lifetime access to the recordings and course materials.
How is this different from a general AI course?
Most general AI courses stop at prompting or a single RAG app. This one devotes eight of its 18 modules to building agents, then covers deployment, security, and enterprise integration.
What to read next
If you’re still comparing options, Best Agentic AI BootCamp, Course and Training Program in 2026 compares programs side by side. If you’re earlier in the decision and want to know where agentic AI skills lead, read Agentic AI Engineer Roadmap: Skills, Tools, and Career Path in 2026. Upcoming cohort dates are on the bootcamp page.