Python for AI Agents: Build And Deploy Agent From Scratch

About this course

This course contains the use of artificial intelligence. All lectures use AI-generated voice narration.

You can build an AI agent in a few lines with a framework. But when it does something unexpected, would you know why?

This course shows you what's actually going on inside an agent - by building one yourself, from a 30-line rule-based loop all the way to a real Gemini-powered agent deployed on AWS Bedrock AgentCore with Terraform, monitored, and torn down again, on screen.

The screen recordings were captured while writing and running the code, not edited afterward to skip the mistakes. When something breaks -- a wrong log group, a retry that never fires, a rate limit hit -- you see the bug and the fix, because that's how you learn what the code does.

You'll build, in order:- A minimal rule-based agent using nothing but the Python standard library- A reusable perceive-think-act loop- A tool-calling system (calculator, clock, text utilities) with a registry and dispatcher- Short-term memory (conversation history / context windows) and durable long-term memory (crash-safe atomic file writes)- A ReAct-style reasoning loop and a multi-step planner, from scratch, no framework- A real agent backed by Google's Gemini API, choosing which of your tools to call- A production deployment of that agent to AWS Bedrock AgentCore, provisioned with Terraform- Retry logic that survives real rate limits - you'll fire concurrent requests, hit a real 429, and watch it recover- A hardened, secured version: secrets in AWS Secrets Manager, least-privilege IAM, real CloudWatch monitoring - and a real terraform destroy to close it all out

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