Autonomous AI QA Agent
An AI agent that turns test tickets into executed, reported test runs across web platforms.
Overview
An autonomous testing agent that reads a ticket, researches context, plans a test, and then drives a real browser to execute it — posting results back automatically. It combines large language models with browser automation and load-testing tooling to extend a small QA team's reach.
Tech Stack
The Challenge
Manual exploratory and regression testing across complex web platforms is slow and hard to scale. The goal was an agent that could take a ticket, understand what needs testing, carry out the steps in a real browser, and report findings — with the option to run on private, local models for cost and data control.
Our Approach
The agent runs a three-phase workflow — research, test plan, then smoke and exploratory testing — orchestrated over multiple LLM backends (hosted and local) and a Playwright-driven browser via the Model Context Protocol. It can generate and run load tests, and it learns reusable patterns from existing automated tests. Results, plans, and screenshots are posted back into the team's issue tracker.
The Result
A reusable automation harness that offloads repetitive testing from engineers, runs on either cloud or on-premise models, and keeps a clear audit trail of what was tested and what it found.
Key Features
- Three-phase workflow: research → test plan → execution
- Drives a real browser through Playwright (Model Context Protocol)
- Runs on hosted or private local LLMs for cost and data control
- Generates and executes load tests
- Learns reusable patterns from existing test suites
- Posts plans, results, and screenshots back to issue tracking
Have a project in mind?
Let's talk about how we can design, build, and ship it together.