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Autify Inc., which provides “Autify,” a platform that leverages AI to support the entire software development lifecycle from requirements to testing, has announced the launch of its next-generation quality assurance (QA) managed service, “Autify AI Coworker,” in Japan. A key feature of this new service is the fusion of the scalability of AI technology and the quality strategy and best practice strategy leadership of QA professionals. Through co-creation between AI agents and professionals, it realizes faster and more accurate quality assurance (QA) in software development, solving the structural challenges of “speed,” “cost,” and “quality” inherent in manual testing processes.
Autify AI Coworker provides autonomous test design, execution, and maintenance by AI agents, along with human-directed and decision-making testing methodologies. Users simply submit a test request, and QA professionals utilize AI agents to execute and review the tests, delivering a test results report. By leveraging the strengths of both humans (“direction” – defining the “why” and value) and AI agents (“execution” – completing the tests with speed and scale), it provides end-to-end support for transitioning to a scalable QA system.
While AI is accelerating the software development cycle, the testing process still relies heavily on manual work and visual inspection, leading to increased testing workloads. Traditional manpower-intensive methods and outsourcing (BPO) are insufficient to avoid depleting engineering resources and prolonging lead times. The difficulty of balancing comprehensiveness with quality—where prioritizing speed leads to missed deadlines and quality declines—further increases the burden on the field. Amidst recruitment difficulties and rising labor costs, building a scalable QA system that does not rely on manual labor is a promising solution for stabilizing quality and maximizing the ROI of quality investments.
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Three strengths and implementation benefits of Autify AI Coworker
Outstanding execution power (speed): Reduces test execution time by up to 86%.
Our proprietary AI agent executes 2 to 10 tasks in parallel, significantly reducing testing time compared to manual execution. Tests that would take weeks for a human can be completed in days, avoiding critical delays in time to market. Test execution is possible 24/7, 365 days a year, allowing for scheduled automated testing.
Relief from workload (cost): Up to 49% reduction in human resource costs.
In the verification conducted by Autify, we were able to cover up to 90% of areas that were difficult for conventional automation tools to handle, such as state-dependent UI operations, scenarios involving security constraints, and complex operations dependent on the browser environment. In addition, while manual testing requires human intervention at every stage, human time is only required for reviewing the results. As a result, by using Autify AI Coworker, tasks that previously took an hour with manual testing can be reduced to about 30 minutes, effectively halving human costs.
Integrating best practices (quality): Raising the overall quality of testing
Standardizing the test design process accelerates automation and replacement with AI agents, enabling a highly reproducible QA system that is not limited by individual skills or resources. The expertise and best practices of Autify professionals further enhance quality. By continuously utilizing these systems, users accumulate knowledge and user data, accelerating the quality improvement cycle. Autify AI Coworker builds a quality foundation as an “organizational asset.”
Autify advocates and promotes QA-AX (Quality Assurance AI Transformation), which aims to end man-dependent QA structures and transform quality data into a foundation for management and development decision-making. This frees engineers from the burden of practical tasks, allowing them to focus on higher-level decision-making. Through QA-AX, Autify helps companies break away from traditional QA models bound by man-month costs and transition to a strategic QA system that is not constrained by resource limitations.
SOURCE: PRTimes
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