What this lesson covers
What it is, what a system prompt is made of, and why zero-shot isn't enough
A practical, ground-up introduction to AI agents. Understand the ReAct loop, tools, orchestration, memory, and evaluation, then build real agents using Aurora Trading Agent platform as your lab.
Explain the difference between a bare language model, a chatbot, and an AI agent
Learning outcome for this course.
Describe the ReAct loop (Thought → Action → Observation) and why it makes agents agentic
Learning outcome for this course.
Design tool-calling workflows and understand why tools, function calling, and MCP servers are all the same core concept
Learning outcome for this course.
Use autonomy controls (whitelists, approvals) to safely run agents in production
Learning outcome for this course.
Connect memory, scheduling, and subagents into a full autonomous workflow
Learning outcome for this course.
Build traces and evaluators (including LLM judges) to measure agent quality
Learning outcome for this course.
Watch the lesson
This free lesson’s video is available in the course player. Watch it before proceeding to the related activities.