Master Agentic AI for QA + Build Custom Agents - MAR'26

2026-ONLY COURSE on Building AI Agents from Scratch with PYTHON + OPENAI + GEMINI + OLLAMA + AGENT OBSERVABILITY for QA

[THE ENTIRE COURSE HAS BEEN CREATED IN 2026 MARCH WITH LATEST AI FRAMEWORKS AND BEST PRACTICES]

Are you ready to invest 7 Hours to build your own AI Agents from scratch and future-proof your QA career?

Welcome to the ONLY course on Udemy that teaches you to build custom AI Agents specifically for QA workflows using vanilla Python - no expensive tools, no black boxes, complete control.

This course is designed for QA Engineers who want to harness the power of Agentic AI without relying on costly no-code platforms like n8n or Zapier. You'll learn to build intelligent agents that actually understand your testing needs.

What is Agentic AI?

Agentic AI refers to autonomous AI systems that can plan, reason, and take actions to achieve specific goals. Unlike simple chatbots, agents can break down complex tasks, make decisions, and execute multi-step workflows - perfect for QA automation.

Why Build Agents with Code Instead of No-Code Tools?

- Full Control: Debug and customize every aspect of your agent's behavior

- Cost Savings: No monthly subscriptions to expensive platforms

- Production Ready: Deploy agents in your CI/CD pipelines

- Deep Understanding: Know exactly how your AI automation works

- Flexibility: Switch between OpenAI, Gemini, or local Ollama models instantly

What Makes This Course Different?

Every single line of code is pushed to GitHub with tags after each topic. You can jump to any point in the course and see exactly how the project looked at that stage - perfect for reviewing and debugging.

Who Should Take This Course?

- QA Engineers wanting to automate test case generation

- Manual Testers looking to analyze logs with AI

- Python Programmers ready to build AI-powered tools

- Tech Leads evaluating AI integration for testing workflows

- Anyone curious about practical Agentic AI applications

What You'll Build:

1. TestCase Generator Agent - Feed it requirements, get comprehensive test cases instantly

2. Log Analyzer Agent - Upload production logs, receive root cause analysis and recommendations

3. Agent Observability System - Track tokens, costs, performance, and errors in real-time

Course Content Breakdown:

Environment Setup

- Python 3.11+ installation and virtual environment

- OpenAI, Google Gemini, and Ollama setup

- Requirements.txt and dependency management

Project Structure

- Professional folder structure for agent projects

- Data folders and output directories

- Environment variable configuration

Core Infrastructure

- Multi-provider LLM client (OpenAI, Gemini, Ollama)

- HTTP API integration with error handling

- Reusable utility functions

- Environment-based model switching

TestCase Generator Agent

- Real-world requirement parsing

- Prompt engineering for test case generation

- Structured JSON output with validation

- CSV export for test management tools

- Command-line interface for flexibility

Log Analyzer Agent

- Production log file analysis

- Error detection and root cause identification

- Technical analysis for developers

- Executive summary for non-technical stakeholders

- JSON reporting for downstream systems

Agent Observability

- Python logging framework integration

- LLM call tracking (tokens, duration, cost)

- Real-time cost calculation for OpenAI and Gemini

- Performance metrics and summary reports

- Error handling and failure detection

Why Agentic AI Matters for QA in 2026:

AI is transforming testing. Companies are already using agents to generate test cases, analyze failures, and predict bugs. QAs who can build and customize these agents will be invaluable. Those who can't will struggle to stay relevant.

This course gives you the skills to build AI solutions tailored to your team's needs - not generic tools that cost $50/month and don't quite fit.

What You Get:

- 7+ hours of hands-on coding tutorials

- Complete source code on GitHub with tags for each topic

- Real-world QA scenarios and practical examples

- Support for OpenAI GPT-4o, Google Gemini 2.0, and local Ollama models

- Production-ready code patterns

- Lifetime access and free updates

Technical Stack:

- Python 3.11+

- OpenAI API (GPT-4o, GPT-4o-mini)

- Google Gemini API (Gemini 2.0 Flash)

- Ollama (Local LLM hosting)

- HTTPX for API calls

- Python logging framework

- Pandas for data handling

- JSON for structured outputs

By the End of This Course:

✓ Build custom AI agents from scratch

✓ Integrate multiple LLM providers in one project

✓ Implement production-ready error handling

✓ Track costs and performance of AI agents

✓ Deploy agents in real QA workflows

✓ Understand when to use code vs no-code tools

✓ Confidently discuss Agentic AI in interviews

Prerequisites:

You should know Python basics - variables, functions, loops, file handling, and modules. If you're new to Python, take my "Master Python Zero to Pro + Real World Projects" course first.

Why Wait? Start Building Your First AI Agent Today!

Enroll now and gain the skills that will define the future of QA automation.

See you inside the course!