Agentic AI Bootcamp: AI Agents with Python, n8n, MCP & RAG

Agentic AI Bootcamp: AI Agents with Python, n8n, MCP & RAG-Complete Guide

Agentic AI Bootcamp: AI Agents with Python, n8n, MCP & RAG is a comprehensive, full-stack course designed to take learners from zero to production-ready AI agent systems. It combines pro-code AI engineering (Python, Pydantic AI, OpenAI SDK, LangChain) with low-code automation (n8n, Flowise) to build autonomous agents, RAG systems, voice assistants, and AI-powered web applications.

Unlike courses that focus on a single framework, this bootcamp emphasizes real-world AI engineering workflows—from local, privacy-first AI setups to scalable, cloud-deployed agentic systems. The course also covers modern standards like Model Context Protocol (MCP) and integrates multiple agent frameworks to future-proof learners for 2026 and beyond.

Course Snapshot

• Instructor: Arnold Oberleiter

• Students Enrolled: 909

• Rating: 4.7 / 5

• Content Length: ~32.5 hours

• Skill Level: Beginner to Advanced

• Language: English (Auto captions)

• Certification: Certificate of completion included

• Access: Full lifetime access (mobile & TV supported)

What This Course Actually Covers

This bootcamp teaches end-to-end Agentic AI development, covering everything from core AI engineering fundamentals to advanced multi-agent orchestration. Learners build Python-based AI agents, integrate low-code automations, design RAG pipelines, deploy voice agents, and create custom AI frontends.

A key focus is on bridging local AI and cloud AI, enabling learners to run models privately on their own machines or deploy them at scale for real businesses.

The Agentic AI Engineering Masterclass 2026

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Skills & Concepts You’ll Work With

AI Engineering Foundations

• Python-based AI development using Cursor, Git, and GitHub

• Pydantic AI for structured outputs, validation, and guardrails

• Building evaluation frameworks and safe AI workflows

OpenAI & Agent Frameworks

• OpenAI AgentKit, Agent Builder, and ChatKit integration

• CrewAI, AutoGen, Semantic Kernel, and multi-agent orchestration

• Deep research agents that browse, reason, and synthesize reports

Model Context Protocol (MCP)

• Understanding MCP for agent-to-tool communication

• Connecting Claude Desktop, Cursor, and local files via MCP

• Building custom MCP servers using Python and n8n

Low-Code Automation with n8n

• Webhooks, HTTP nodes, loops, error handling, and orchestration

• Building multi-agent systems and orchestrators

• Creating Telegram bots and self-hosted automation workflows

Retrieval-Augmented Generation (RAG)

• Designing and optimizing RAG pipelines

• Embeddings, chunking strategies, Tok-K, temperature, Top-P

• Vector databases: Supabase, Pinecone, Postgres

Local AI & Privacy-First Systems

• Running local LLMs with Ollama, Docker, and Flowise

• Using DeepSeek, Qwen, LLaMA, Kimi, and other OSS models

• Building privacy-preserving AI systems without cloud dependency

Voice & Multimodal AI

• Real-time voice agents using LiveKit, Vapi, and ElevenLabs

• Image & video generation workflows using ComfyUI, SDXL, Flux

• Automating generative pipelines via n8n

Full-Stack AI Deployment

• AI web apps with Next.js, React, TypeScript, Tailwind CSS

• Hosting on Render, GitHub, and Docker-based environments

• Deploying custom AI chatbots directly into websites

AI Business, Security & Compliance

• Building and pricing an AI agency

• Lead generation, sales strategy, and niche discovery

• GDPR, EU AI Act compliance, prompt injection defense, and data security

Who This Course Is Best Suited For

• Aspiring AI Engineers and AI Automation Developers

• Full-stack Developers entering AI and Agentic workflows

• No-code / low-code builders scaling into pro-code AI

• Entrepreneurs building AI products or agencies

• Students and professionals preparing for AI roles in 2026+

Common Questions Learners Ask

Do I need prior AI or coding experience?
No. Everything is explained step by step, from basics to advanced topics.

Is this more low-code or pro-code?
Both. The course intentionally bridges n8n & Flowise with Python, OpenAI SDK, and full-stack development.

Does it cover local and cloud AI?
Yes. You’ll build local RAG systems and deploy scalable cloud-based agents.

Is this suitable for real business use?
Yes. The course focuses heavily on production workflows, deployment, security, and monetization.

Practical Value

What sets this course apart is its breadth and integration. Learners don’t just experiment with agents—they build complete AI systems: autonomous agents, RAG pipelines, voice assistants, web apps, and business-ready automations.

By combining Agentic AI, MCP, n8n, and full-stack deployment, the skills learned here map directly to how AI products are being built and shipped in real companies today.

Final Thoughts

If you’re serious about building—not just using—AI, this bootcamp offers one of the most complete learning paths available. It’s ideal for learners who want to master Agentic AI, automation, and AI product development in a single, cohesive program.