KushoAI launches API testing framework

By Milan Rojan

KushoAI has introduced an API Testing Maturity Model designed to help enterprises strengthen software testing capabilities as AI accelerates software development and deployment.

The framework has been developed to provide engineering teams with a structured roadmap for assessing and improving API testing practices, addressing the growing gap between software delivery speed and application reliability. According to the company, the model has focused on helping organisations move beyond conventional testing approaches towards continuous, AI-assisted software validation.

Abhishek Saikia, Co-founder and CEO of KushoAI, said: “The conversation around AI has largely focused on generating software faster. The next challenge is ensuring that software continues to work reliably as systems become more dynamic and interconnected.”

The API Testing Maturity Model has outlined a five-stage framework that enables organisations to benchmark their current testing capabilities while progressing from foundational practices, such as contract validation and functional automation, to more advanced capabilities including risk-based coverage, continuous validation and self-healing test suites.

KushoAI has said the framework has addressed the increasing adoption of API-first architectures and AI-generated code, where conventional testing methods may struggle to identify complex business logic and cross-field issues before software reaches production environments.

Alongside the framework, KushoAI has expanded its software quality initiatives over the past year. The company has introduced APIEval-20, an open benchmark for assessing AI agents on API bug detection, published research comparing AI coding tools against complex API defects and launched a Test Readiness Score for OpenAPI specifications.

The API Testing Maturity Model has been made available as a free resource for engineering leaders, architects, platform teams and developers seeking to benchmark and modernise their API testing strategies. The initiative has reflected increasing demand for AI-enabled software quality frameworks as organisations continue to accelerate application development while seeking to maintain reliability and resilience.