AI Agents for QA Engineers

Choose Your Learning Path

Start building AI agents for QA today. All tiers include lifetime access to course materials.

Starter

Self-paced learning with all course materials

₹3,499

One-time payment

All 17 modules
5 project templates
Lifetime access
Theory + build videos
Community support

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Pro

Includes code reviews and direct feedback

₹7,999

One-time payment

Everything in Starter
Code reviews for all 5 projects
Direct feedback from instructors
Priority support
Certificate of completion

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Cohort

Live batch with real-time sessions

₹16,000

Next batch starts May 2026

Everything in Pro
Live sessions with instructors
Real-time Q&A
Peer learning community
Job placement support

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Global pricing: $29 (Starter) / $69 (Pro)

Complete Course Curriculum

17 modules • 4 sections • Theory + Cursor build + your turn challenge

Section 0: Orientation & Setup

2 modules ~45 min Everyone starts here

01 Why AI agents matter for QA right now concept

What agents actually are, where QA is heading, how this course is structured

02 Environment setup with Cursor + Python build

Install Cursor, set up venv, get first LLM API call working in 10 min

Section 1: LLM Fundamentals for QA Engineers

3 modules ~1.5 hrs

Freshers focus — experienced can skim

03 LLM APIs demystified concept

OpenAI, Anthropic, Gemini — how they work, when to use which, cost basics

04 Prompt engineering for testers build

Write prompts that generate test cases, edge cases, and bug reports reliably

05 Giving agents tools & memory build

Function calling, tool use, short-term vs long-term memory — with live demo

Section 2: Core Agent Frameworks

3 modules ~2 hrs

Experienced QA engineers jump in from here

06 LangChain essentials — just what you need build

Chains, tools, memory — skip the bloat, only the 20% actually used in real projects

07 CrewAI — multi-agent teams build

Roles, tasks, crews — build a 3-agent QA team that works together autonomously

08 LangGraph for stateful workflows concept

When agents need branching logic, retries, human-in-the-loop approvals

Section 2.5: MCP Basics

2 modules ~1 hr

Simple introduction to MCP

09 What is MCP for QA engineers concept

Simple explanation: What MCP is, why it exists, basic use cases for QA

10 Simple MCP integration in your IDE build

Connect a basic MCP tool in VS Code, Cursor, or Kiro — 15 minute demo

Section 3: 5 Real-World Projects

5 modules ~4 hrs

The core value of the course

P1 Test case generator agent project

Feed it a user story or PRD → get 20 structured test cases with edge cases instantly

P2 Bug report agent project

Paste a failed log or error → get a formatted, Jira-ready bug report with steps to reproduce

P3 Self-healing test monitor project

Agent watches CI test results, detects flaky tests, suggests fixes automatically

P4 QA crew — requirements to tests project

3-agent CrewAI system: one reads requirements, one writes tests, one reviews them

P5 QA copilot — full pipeline project

End-to-end agent: reads PR → generates tests → runs them → posts result to Slack/Jira

Section 4: Ship It & Get Paid

3 modules ~1.5 hrs

Career + freelance

14 CI/CD integration build

Plug your agents into GitHub Actions, Jenkins — runs automatically on every PR

15 Portfolio & GitHub setup career

How to present these 5 projects so hiring managers and clients actually notice

16 Freelance & upskill roadmap career

Where to find paid AI QA projects, how to price yourself, what to learn next

Each module: 5 min theory → 10 min Cursor build → 5 min your turn challenge