Udemy - Agentic AI From Zero to Expert - 100 Real Labs

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size1.7 GB
  • Uploaded Byfreecoursewb
  • Downloads378
  • Last checkedJul. 10th '26
  • Date uploadedJul. 09th '26
  • Seeders 31
  • Leechers32

Infohash : 41F440DD564B45AE6BAC5B2B81C58B48A790CFF9

Agentic AI From Zero to Expert: 100 Real Labs

https://WebToolTip.com

Published 6/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 7h 51m | Size: 1.72 GB

From simple AI prompts to production autonomous agents using LangGraph, RAG, MCP, Kubernetes, and AI Ops.

What you'll learn
Build multi-agent platforms that collaborate, review, validate, and coordinate work.
Architect production-grade autonomous AI agents using modern agent engineering principles.
Build reasoning, planning, memory, and tool-using agents from scratch.
Master LangGraph workflows for stateful and reliable agent execution.
Design enterprise RAG systems using embeddings, vector databases, and hybrid retrieval.
Develop MCP-based tool ecosystems that safely connect agents to real systems.
Implement security, governance, audit logging, and compliance controls for AI systems.
Deploy scalable AI workloads using Docker, Kubernetes, Terraform, and GitOps.
Monitor, evaluate, and troubleshoot agents using OpenTelemetry, Prometheus, and Grafana.
Construct a sovereign enterprise AI platform in the PhD-level Lab 100 capstone.

Requirements
Recommended Requirements
1. Basic computer literacy.
2. No previous AI experience required.
3. Basic Python knowledge is helpful but not mandatory.
4. Familiarity with command-line basics can accelerate learning.
Recommended Hardware
1. 16 GB RAM
2. Quad-core CPU
3. 100 GB free storage

Files:

[ WebToolTip.com ] Udemy - Agentic AI From Zero to Expert - 100 Real Labs
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction to Agentic AI From Zero to Expert 100 Real Labs
    • 1 - Introduction.mp4 (90.8 MB)
    • 1 - The_Agentic_AI_Quest.pdf (17.6 MB)
    10 - Module 9 — Observability and Reliability
    • 100 - Lab 90. AI Reliability Platform.html (14.7 KB)
    • 90 - Observability and Reliability.mp4 (148.8 MB)
    • 91 - Lab 81. OpenTelemetry Fundamentals.html (12.8 KB)
    • 92 - Lab 82. Agent Tracing.html (12.9 KB)
    • 93 - Lab 83. Metrics Collection.html (13.8 KB)
    • 94 - Lab 84. Prometheus Integration.html (13.5 KB)
    • 95 - Lab 85. Grafana Dashboards.html (15.2 KB)
    • 96 - Lab 86. Agent Evaluation Frameworks.html (14.8 KB)
    • 97 - Lab 87. Hallucination Detection.html (15.0 KB)
    • 98 - Lab 88. Reliability Testing.html (14.3 KB)
    • 99 - Lab 89. Chaos Engineering for Agents.html (13.9 KB)
    11 - Module 10 — Production Deployment
    • 101 - Production Deployment.mp4 (171.9 MB)
    • 102 - Lab 91. Containerizing Agents with Docker.html (12.9 KB)
    • 103 - Lab 92. Kubernetes Fundamentals.html (14.3 KB)
    • 104 - Lab 93. Agent Deployment on Kubernetes.html (13.9 KB)
    • 105 - Lab 94. GitOps with Argo CD.html (14.2 KB)
    • 106 - Lab 95. Infrastructure as Code with Terraform.html (14.8 KB)
    • 107 - Lab 96. Model Serving with vLLM.html (14.0 KB)
    • 108 - Lab 97. Cost Optimization Strategies.html (14.2 KB)
    • 109 - Lab 98. Sovereign AI Infrastructure.html (14.8 KB)
    • 110 - Lab 99. Decentralized Research Agent Network.html (14.5 KB)
    • 111 - Lab 100. Sovereign Enterprise Agent Platform Capstone.html (15.5 KB)
    12 - Conclusion
    • 112 - Conclusion.mp4 (163.6 MB)
    2 - Module 1 — Foundations and First Success
    • 10 - Lab 8. Open Source Model Ecosystem Overview.html (13.4 KB)
    • 11 - Lab 9. Running Local Models with Ollama.html (13.2 KB)
    • 12 - Lab 10. Initial Success Milestone Local AI Assistant.html (13.9 KB)
    • 2 - Foundations and First Success.mp4 (82.2 MB)
    • 3 - Lab 1. Understanding Agentic AI Architecture.html (16.4 KB)
    • 4 - Lab 2. Local Development Environment Setup.html (14.4 KB)
    • 5 - Lab 3. Python Foundations for AI Agents.html (13.6 KB)
    • 6 - Lab 4. Git and Version Control for AI Projects.html (12.6 KB)
    • 7 - Lab 5. Virtual Environments and Dependency Isolation.html (14.0 KB)
    • 8 - Lab 6. Building the First LLM Application.html (13.4 KB)
    • 9 - Lab 7. Understanding Prompts and Context Windows.html (13.0 KB)
    3 - Module 2 — Core Agent Engineering
    • 13 - Core Agent Engineering.mp4 (152.7 MB)
    • 14 - Lab 11. Agent Lifecycle Fundamentals.html (15.2 KB)
    • 15 - Lab 12. Reasoning Loops and Planning Patterns.html (13.5 KB)
    • 16 - Lab 13. Tool Calling Fundamentals.html (14.0 KB)
    • 17 - Lab 14. Building a Calculator Agent.html (14.0 KB)
    • 18 - Lab 15. Weather Tool Agent.html (14.1 KB)
    • 19 - Lab 16. Search-Augmented Agent.html (14.1 KB)
    • 20 - Lab 17. Memory Fundamentals.html (13.8 KB)
    • 21 - Lab 18. Short-Term Context Management.html (13.9 KB)
    • 22 - Lab 19. Long-Term Agent Memory.html (14.0 KB)
    • 23 - Lab 20. Production Agent Patterns.html (14.5 KB)
    4 - Module 3 — LangGraph Foundations
    • 24 - LangGraph Foundations.mp4 (163.3 MB)
    • 25 - Lab 21. LangGraph Architecture.html (15.0 KB)
    • 26 - Lab 22. Graph Nodes and State.html (13.6 KB)
    • 27 - Lab 23. Conditional Routing.html (14.3 KB)
    • 28 - Lab 24. Agent Workflows.html (15.0 KB)
    • 29 - Lab 25. Stateful Execution.html (14.5 KB)
    • 30 - Lab 26. Error Recovery Flows.html (13.8 KB)
    • 31 - Lab 27. Multi-Step Planning.html (12.8 KB)
    • 32 - Lab 28. Dynamic Tool Selection.html (14.4 KB)
    • 33 - Lab 29. Persistent Graph Memory.html (14.2 KB)
    • 34 - Lab 30. Enterprise Workflow Agent.html (15.1 KB)
    5 - Module 4 — Retrieval and Knowledge Systems
    • 35 - Retrieval and Knowledge Systems.mp4 (149.8 MB)
    • 36 - Lab 31. RAG Fundamentals.html (13.8 KB)
    • 37 - Lab 32. Embedding Models.html (13.2 KB)
    • 38 - Lab 33. Vector Search Concepts.html (14.0 KB)
    • 39 - Lab 34. ChromaDB Deployment.html (12.1 KB)
    • 40 - Lab 35. Qdrant Deployment.html (13.6 KB)
    • 41 - Lab 36. Document Ingestion Pipelines.html (15.6 KB)
    • 42 - Lab 37. Chunking Strategies.html (13.9 KB)
    • 43 - Lab 38. Retrieval Optimization.html (15.0 KB)
    • 44 - Lab 39. Hybrid Search Architecture.html (15.0 KB)
    • 45 - Lab 40. Enterprise Knowledge Agent.html (14.3 KB)
    6 - Module 5 — MCP Engineering
    • 46 - MCP Engineering.mp4 (148.4 MB)
    • 47 - Lab 41. MCP Architecture.html (16.0 KB)
    • 48 - Lab 42. MCP Server Fundamentals.html (13.0 KB)
    • 49 - Lab 43. MCP Client Fundamentals.html (14.1 KB)
    • 50 - Lab 44. Local Tool Exposure.html (13.0 KB)
    • 51 - Lab 45. File System MCP.html (13.5 KB)
    • 52 - Lab 46. Database MCP.html (14.0 KB)
    • 53 - Lab 47. API MCP Integration.html (13.2 KB)
    • 54 - Lab 48. Secure MCP Design.html (15.0 KB)
    • 55 - Lab 49. Multi-Tool MCP Ecosystem.html (14.4 KB)
    • 56 - Lab 50. Enterprise MCP Gateway.html (14.8 KB)
    7 - Module 6 — Multi-Agent Systems
    • 57 - Multi-Agent Systems.mp4 (162.9 MB)
    • 58 - Lab 51. Multi-Agent Design Principles.html (15.8 KB)
    • 59 - Lab 52. Agent Communication Models.html (15.7 KB)
    • 60 - Lab 53. Planner Agents.html (15.1 KB)
    • 61 - Lab 54. Research Agents.html (15.3 KB)
    • 62 - Lab 55. Execution Agents.html (15.6 KB)
    • 63 - Lab 56. Reviewer Agents.html (14.3 KB)
    • 64 - Lab 57. Consensus Mechanisms.html (13.4 KB)
    • 65 - Lab 58. Agent Swarm Patterns.html (17.8 KB)
    • 66 - Lab 59. Distributed Agent Workflows.html (14.3 KB)
    • 67 - Lab 60. Multi-Agent Research Platform.html (14.5 KB)
    8 - Module 7 — Data Engineering for Agents
    • 68 - Data Engineerin

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