[100% Off] Agentic Ai Engineering: Multi-Agent Systems Mastery
Build production agents with LangGraph, CrewAI, AutoGen, tool use, memory, MCP & human-in-the-loop
Requirements
- Intermediate Python (functions, modules, virtual environments; async helpful but taught lightly)
- Basic experience calling an LLM API (OpenAI-compatible or Gemini) and reading JSON
- A computer where you can install Python 3.11+, create a venv, and set API keys in a .env file
- Willingness to spend a small amount on API calls or use free/cheap model options when shown
- No prior LangGraph, CrewAI, or AutoGen experience required — we build up from scratch
Description
This course contains the use of artificial intelligence.
AI is used to reframe the words, fixing spelling mistakes and grammatical mistakes and audio conversion.
Most courses either demo a single chatty agent or drown you in framework docs. This masterclass is different: you learn to engineer agentic systems that plan, use tools, keep state, hand off work, and stay under human control — then you ship them with guardrails, evaluation, and cost discipline. It is built for Python-comfortable developers, automation engineers, and technical operators who need production-minded agents, not toy demos. You start from first principles with a pure Python agent loop, then go deep on LangGraph for stateful workflows. You design multi-agent architectures on purpose — supervisor, sequential, swarm — and you practice the same skills in CrewAI and AutoGen so you can choose the right tool instead of following hype. You connect tools through MCP, use retrieval as a tool rather than a magic box, and put layered guardrails and trajectory evals around everything that can spend money or touch a customer.
Three hands-on projects run through the course: a research multi-agent team, a support triage crew, and a full ops-exception capstone with human-in-the-loop and cost caps. Every major concept includes a global dollar example and an India rupee example so the economics feel real whether you bill in USD or INR. Practice files ship as section zip archives that match the code on screen — download, run, and break things safely. I teach as a practitioner with more than twenty years getting automation adopted on real floors and back offices — warehouses, manufacturing, supply chain — where a wrong tool call is not a cute log line, it is a late truck or a bad stock write-off. No employer names, only honest patterns. If you want a complete engineering path from first agent to multi-agent production, enrol and build systems you can defend to a skeptical lead engineer and a CFO who asks what each run costs.
Author(s): Ganesh Ravikumar
