Learn Agentic AI Fundamentals

Before you build anything, you need a clean mental model of what sits where. AI is the umbrella. ML is pattern learning. Deep learning is ML with neural networks. GenAI creates new content. RAG grounds GenAI in your data. Agents give GenAI tools and goals.

Tokenize → embed → attention → feed-forward → predict the next token, repeat. That is the whole trick. The magic is attention: every token looks at every other token and decides what is relevant, in parallel.

Four levers, radically different cost and control. Prompting: minutes, cheap, no data moat. RAG: days, grounds answers in your docs. Fine-tuning: weeks, shapes tone and format. Training from scratch: quarters and millions of dollars.

Every real agent is the same set of moving parts: an LLM doing the reasoning, a planning loop (thought → evaluation → selection → execution), layered memory (working, semantic, episodic, procedural), a decision procedure, and tools/MCP connecting it to the environment. Strip any of those out and you do not have an agent — you have a chatbot.

Agents do not just answer — they reason. Chain-of-Thought walks the problem step by step. Tree-of-Thought explores branches and prunes. Graph-of-Thoughts goes further, aggregating across paths in a graph. Reflexion critiques its own output and retries with verbal self-feedback.

The full production stack, end to end: an orchestration layer routing to local and remote agents, MCP servers connecting tools, a knowledge layer with structured and vector data, plus observability and evaluation threaded through every hop.

Agents need more than a context window. Working memory holds the current task. Semantic memory stores facts. Episodic memory remembers past interactions. Procedural memory encodes how to do things. Meta memory — memory about memory — is what lets the agent reflect on what it knows and tune its own retrieval.