Python for AI Agents: Build And Deploy Agent From Scratch

Python for AI Agents: Build And Deploy Agent From Scratch
Last updated 9/2026
Created by Prasad Yarra
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 13 Lectures ( 1h 54m ) | Size: 1021.9 MB

Perceive-think-act, tools, memory, ReAct, and a real Gemini agent - deployed to AWS and torn down on screen.

What you'll learn
Build a working AI agent in plain Python with zero frameworks and zero external dependencies
Implement the perceive-think-act loop that underlies every agent architecture
Give an agent real tools using a function-calling pattern with a registry and dispatcher
Design short-term (context window) and crash-safe, atomic-write long-term memory for an agent
Implement the ReAct reasoning pattern and multi-step task planning from scratch
Connect a real LLM (Google Gemini) and let it choose which tools to call
Deploy a production agent to AWS Bedrock AgentCore using Terraform
Add retry logic that survives real rate limits, and harden a deployment with Secrets Manager, least-privilege IAM, and CloudWatch monitoring

Requirements
Comfortable with core Python: functions, classes, dictionaries, basic file I/O
No prior AI/ML or cloud experience needed
A computer that can run Python 3.10+ (any OS)
For Module 5 only: a free Google Gemini API key and an AWS account (only needed if you want to follow the live deployment lessons yourself)

Description
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

Along the way you also get

Hands-on exercises for every module, with worked solutions

A short quiz plus a "debug this" challenge after every lesson

A capstone project - a Research Notes Agent combining tools, durable memory, and multi-step Gemini reasoning in one build, with a starter (TODOs included) and a finished solution

If you already know some Python and want to understand how an AI agent actually works by building one yourself, this course is for you.

Who this course is for
Python developers who want to understand how AI agents work under the hood, not just call a framework
Developers evaluating agent frameworks (LangChain, Strands, etc.) who want to know what those frameworks are doing for them
Anyone who wants a real, working example of deploying and hardening an LLM agent in production on AWS

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