Multi-agent AI Testing for Retail Enterprises

Mindfire developed a Multi-Agent AI Testing System for a leading US-based retail company to enable continuous, intelligent testing of its enterprise application. The platform supports critical business functions including order management, patient services, point-of-sale (POS), reporting, and ERP integration.

Prior to implementation, every requirement created in Azure DevOps demanded several hours of manual effort to author test cases, build test plans, and create BDD feature files, These tasks required in-depth knowledge of over 2,000 existing automation step definitions. To eliminate this bottleneck, Mindfire introduced a Multi-Agent AI Testing System comprising of four specialized AI agents. These are operated by the Manual QA team and powered by GitHub Copilot / Claude Code, and deterministic Node.js scripts.

The agents now automatically generate manual test cases, create Azure DevOps test plans, produce BDD Gherkin feature files, and generate live test data. The resulting feature files are then routed to the automation repository’s PendingFeatures folder, where Mindfire’s SDET team implements, validates and executes them.

This AI-driven workflow reduced the end-to-end effort per feature from 4–6 hours to approximately 35–45 minutes, significantly accelerating test design, automation readiness, and release cycles.

Client Name: Confidential
Industry: Retail, Ecommerce
Location: USA

QA Automation & AI Enablement

Node.js, GitHub Copilot / Claude Code, steering documents; Azure DevOps, Confluence, Figma; C# / .NET 8, SpecFlow (BDD), NUnit, Selenium WebDriver, Page Object Model; Microsoft SQL Server.