Free self-paced course

Advanced Agentic AI & Knowledge Systems Course

This advanced Agentic AI and knowledge systems course teaches how to build bounded, observable, and evidence-grounded agents. It connects agent loops, Model Context Protocol, durable state, execution harnesses, retrieval, ontologies, knowledge graphs, GraphRAG, evaluation, guardrails, and production operations.

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For

Experienced developers and AI engineers who want formal architecture, implementation depth, failure analysis, evaluation, and open-ended system projects.

Study time

65–80 hours

Prerequisites

Python, APIs, testing, basic LLM concepts, and comfort reading technical specifications.

What you will learn

  • Engineer bounded agent loops with typed state, budgets, checkpoints, and recovery.
  • Build reproducible agent harnesses with isolation, traces, replay, and evaluation hooks.
  • Implement retrieval, RAG, ontology, knowledge-graph, and GraphRAG systems.
  • Defend a knowledge-driven agent with evaluation evidence, security controls, and operational limits.

Recommended starting guides

Use these answer-first lessons to build the concepts this course depends on.

Understand bounded agent loops, tools, and memoryDesign safe Model Context Protocol tools and resourcesEngineer a reproducible AI agent harnessDesign and evaluate a production GraphRAG system

Course format

Each topic combines a concise explanation, an animated mechanism, hands-on evidence, and an optional book-level deep dive.

  • Agentic AI and Loop Engineering
  • MCP and structured model interfaces
  • Harness Engineering and sandboxes
  • RAG, ontologies, and GraphRAG
  • Ontology-grounded agent capstone

Mastery evidence

Ontology-Grounded GraphRAG Agent

Build a GraphRAG-enabled agent inside a reproducible harness with budgets, evaluations, failure recovery, and trace evidence.

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