BeginnerStart here. No prior knowledge assumed.
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Lesson 1
What is Agentic AI?
Agents versus assistants, and what changes when a model can act.
Lesson 2
What is MCP?
Servers, clients and hosts, and the integration problem the protocol solves.
Lesson 3
RAG
Giving agents grounded knowledge rather than relying on training data.
Lesson 4
Embeddings and Vector Databases
The retrieval layer most agent systems depend on.
Lesson 5
Prompt Injection and AI Security
Why tool access turns a content problem into a security problem.
What problem does MCP solve?
Why does tool access raise the security stakes?
IntermediateBuild on the basics.
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Lesson 1
LangChain
The framework, its abstractions, and when they help versus obscure.
Lesson 2
LlamaIndex
Retrieval-focused framework for document-heavy agent systems.
Lesson 3
CrewAI
Multi-agent orchestration and where role-based agents fit.
Lesson 4
AutoGen
Conversational multi-agent patterns.
Lesson 5
Manus
A general autonomous agent in practice, and verifying its output.
What is the distinction between MCP tools and resources?
When do framework abstractions hurt?
AdvancedDepth, edge cases and strategy.
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Lesson 1
Reasoning Models
When extra inference compute improves agent reliability.
Lesson 2
AI Evals and Benchmarks
Evaluating agent systems, where failure is multi-step and compounding.
Lesson 3
Token Economics
Agentic loops consume tokens fast — controlling the cost.
Lesson 4
Devin
Autonomous coding agents, and the honest gap between demos and completion rates.
Lesson 5
AI Regulation
Governance obligations when an AI system takes consequential actions.
What is the most reliable prompt injection mitigation?
Why do agent failures compound?