Agentic AI for QA Automation with Python
Orchestrate multi-agent QA workflows in Python with AutoGen, state control and human approval gates.
- Duration
- 2.5 hours
- Lectures
- 20
- Level
- All Levels
- Learners
- Active learners
- Python
- AutoGen
- Multi-agent orchestration
- Async Python
About this course
Orchestrate AI-assisted QA agents with AutoGen in patterns that survive contact with real systems.
What you'll learn
- AutoGen multi-agent QA
- Practical orchestration patterns
- Python-first implementation
What you'll build
A multi-agent QA workflow in AutoGen
Specialised agents that plan, execute and review
Termination conditions and human-in-the-loop approval gates
Project architecture
The pipeline you assemble across the course:
- Testing goal
- Planner agent
- Executor agent
- Reviewer agent
- Human approval gate
- Result + termination condition
Curriculum overview
20 lectures · 2.5 hours of on-demand video.
01Agentic foundations
- Single prompts vs agent systems
- Roles and responsibilities
02AutoGen in practice
- Defining agents and conversations
- Async orchestration
03Control & safety
- State management and termination logic
- Human-in-the-loop approvals
FAQ
Why multi-agent instead of one prompt?
Because real QA tasks need planning, execution and review — separating them makes results reviewable and repeatable.
Is it safe to let agents act?
Only with guardrails. Termination conditions and approval gates are a core part of the course.
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Ready to start Agentic AI for QA Automation with Python?
Lifetime access, 20 lectures, and a project you can put on your CV.