From simple AI prompts to production autonomous agents using LangGraph, RAG, MCP, Kubernetes, and AI Ops.
This course contains the use of artificial intelligence. I only charge a fee solely for the time invested in building this comprehensive curriculum.
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..
Course Content
- Introduction to Agentic AI From Zero to Expert: 100 Real Labs –> 1 lecture • 6min.
- Module 1 — Foundations and First Success –> 11 lectures • 39min.
- Module 2 — Core Agent Engineering –> 11 lectures • 46min.
- Module 3 — LangGraph Foundations –> 11 lectures • 47min.
- Module 4 — Retrieval and Knowledge Systems –> 11 lectures • 46min.
- Module 5 — MCP Engineering –> 11 lectures • 48min.
- Module 6 — Multi-Agent Systems –> 11 lectures • 46min.
- Module 7 — Data Engineering for Agents –> 11 lectures • 45min.
- Module 8 — Security and Compliance –> 11 lectures • 47min.
- Module 9 — Observability and Reliability –> 11 lectures • 45min.
- Module 10 — Production Deployment –> 11 lectures • 48min.
- Conclusion –> 1 lecture • 8min.
Requirements
This course contains the use of artificial intelligence. I only charge a fee solely for the time invested in building this comprehensive curriculum.
Stop Building AI Demos. Start Engineering AI Systems.
The internet is currently flooded with “vibe coding.”
Copy a prompt.
Call an API.
Build a chatbot.
Post a demo.
Unfortunately, real companies do not hire engineers to build demos.
They hire engineers who can design reliable systems.
They need AI that can reason, retrieve information, use tools, maintain memory, follow rules, recover from failures, generate audit trails, operate securely, and run in production.
Most AI courses stop at prompting.
Some teach simple chatbots.
A few teach basic RAG.
Very few teach how autonomous AI systems actually work inside real organizations.
This course was built to solve that problem.
A 100-Lab Journey from Beginner to AI Systems Architect
This is not a theory course.
This is not a prompt engineering course.
This is not another “build ChatGPT in 30 minutes” tutorial.
You will complete 100 carefully structured hands-on labs that progressively transform you from a beginner into an engineer capable of building enterprise-grade autonomous systems.
You begin with Python, local models, and your first AI applications.
You then move into:
- Agent reasoning
- Planning systems
- Tool calling
- Memory architectures
- LangGraph workflows
- Retrieval systems
- Vector databases
- MCP integrations
- Multi-agent collaboration
- Event-driven architectures
- Security engineering
- Observability
- Reliability engineering
- Kubernetes deployment
- Sovereign AI infrastructure
Every module builds on the previous one.
Every lab produces real engineering skills.
What’s Inside?
Module 1: Foundations
You build your first local AI assistant and understand how modern agents work.
Module 2: Agent Engineering
You develop reasoning loops, memory systems, and tool-using agents.
Module 3: LangGraph
You learn stateful workflows, execution graphs, recovery mechanisms, and enterprise orchestration.
Module 4: Retrieval Systems
You master embeddings, vector databases, document ingestion, and production RAG.
Module 5: MCP Engineering
You connect agents to files, databases, APIs, and external tools.
Module 6: Multi-Agent Systems
Planner agents.
Research agents.
Reviewer agents.
Collaborative intelligence.
Module 7: Data Engineering
Redis, PostgreSQL, Kafka, structured outputs, and knowledge pipelines.
Module 8: Security and Governance
Prompt injection defense.
RBAC.
Secrets management.
Audit trails.
Compliance.
Module 9: Reliability Engineering
Tracing.
Metrics.
Observability.
Hallucination detection.
Chaos testing.
Module 10: Production Infrastructure
Docker.
Kubernetes.
GitOps.
Terraform.
Model serving.
Cost optimization.
Sovereign deployment.
The PhD-Level Lab 100 Capstone
Most courses end with a chatbot.
This course ends with an enterprise platform.
In Lab 100, you build a complete sovereign autonomous AI environment capable of:
- Multi-agent reasoning
- Enterprise search
- Knowledge retrieval
- Human approval workflows
- Compliance logging
- Observability pipelines
- Local model execution
- Kubernetes deployment
- Multi-user access
- Reliability engineering
- Security enforcement
You will deploy a complete enterprise-grade agent platform using open-source technologies.
This project demonstrates skills that typically require multiple separate courses and years of industry experience.
Why Enroll Now?
The market is rapidly moving beyond simple LLM applications.
Organizations increasingly require engineers who understand:
- Agent architectures
- AI operations
- Reliability engineering
- Governance
- Observability
- Security
- Sovereign deployment
Those skills remain difficult to find.
Most courses still teach yesterday’s AI.
This course teaches the production engineering practices organizations will increasingly expect over the next several years.
If your goal is to move from AI user to AI engineer, from experimentation to architecture, and from prompts to production systems, this course provides the complete roadmap.