Agentic AI Engineering: Multi-Agent Systems Mastery https://WebToolTip.com Published 8/2026 Created by Ganesh Ravikumar MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All Levels | Genre: eLearning | Language: English | Duration: 67 Lectures ( 18h 12m ) | Size: 2.9 GB Build production agents with LangGraph, CrewAI, AutoGen, tool use, memory, MCP & human-in-the-loop What you'll learn ⚡ Explain agentic AI as a goal-driven loop with tools, state, and control — not a single chat reply ⚡ Build a pure-Python agent loop with tool schemas, structured outputs, and recovery from bad results ⚡ Design short-term and long-term memory and state schemas that survive production changes ⚡ Apply plan-execute-replan, reflection, and human-in-the-loop checkpoints with cost-aware limits ⚡ Build stateful LangGraph workflows with routers, checkpoints, streaming, and HITL breakpoints ⚡ Choose single-agent vs multi-agent designs and implement supervisor, sequential, and handoff patterns ⚡ Build multi-agent systems in LangGraph, CrewAI, and AutoGen and pick a framework with a clear matrix ⚡ Connect and author MCP tools and use agentic RAG as retrieve-then-act, not prompt stuffing ⚡ Add layered guardrails, trajectory evaluation, traces, and cost/latency observability ⚡ Package, budget, and ship an ops-exception multi-agent capstone with golden-set evals ⚡ Name and run practice files from section source zips that match on-screen code ⚡ Judge when not to use an agent — and defend autonomy level to technical and business stakeholders 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