What this lesson covers
Where are you with AI today?
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.
Reflection prompts
What's one task you wish AI could do for you automatically? Not just answer a question, but actually take action.
Write your response privately in the course player.
Have you ever tried to get an AI to do something multi-step (like research + summarize + format)? What happened?
Write your response privately in the course player.
What does 'AI agent' mean to you right now? Don't worry about being right. Just write what comes to mind. We'll come back to these answers at the end of the course.
Write your response privately in the course player.