What this lesson covers
See a real tool call happen in Aurora
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.
Practice with Aurora
In the video you just watched, you learned that tools let an AI agent take real actions instead of just generating text. Now see it for yourself. Ask Aurora about a stock and watch what happens: instead of guessing, Aurora calls a tool to look up actual market data and returns a real number. That one tool call is the simplest version of what makes an agent different from a chatbot.
Create an RSI indicator for AAPL
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