AI Agents

Learn the core building blocks of agents, memory types, ReAct loops, multi-agent collaboration, 30 must-know terms, and A2A/AG-UI protocols.

Build a ReAct-style agent in pure Python with zero frameworks — a minimal Agent class, a structured Thought/PAUSE/Action/PAUSE/Observation/Answer system prompt, a manual step-by-step walkthrough, and a fully automated agent_loop() controller.

How agents remember — semantic/episodic/procedural long-term memory types, why memory-less agents are a blank slate every interaction, and the 5-part memory architecture (short-term, long-term, entity, contextual, user).

Two complementary mental models for agentic systems — the 5-level maturity progression from basic responder to fully autonomous agent, and the 4-layer architecture stack from LLM foundation to agentic infrastructure.

Seven distinct architectural patterns for orchestrating multiple specialized agents — Parallel, Sequential, Loop, Router, Aggregator, Network, and Hierarchical — each suited to a different workflow shape.

A hands-on walkthrough building a real-time currency-conversion tool for a CrewAI agent — first as a native BaseTool, then re-implemented as a reusable MCP server consumable by any agent.

A comprehensive glossary of the 30 essential terms for understanding modern AI agents — covering the core reasoning loop, memory, coordination, and emerging protocols.

How agents talk to other agents (A2A) and to user interfaces (AG-UI) — plus the emerging '3 protocols, one stack' landscape alongside MCP.

Using the open-source Opik Agent Optimizer to automatically improve a prompt via an LLM-critique-and-refine loop, instead of manually iterating by hand.