KushoAI, an AI-native software reliability platform, has released a new whitepaper, “Building Adaptive Coverage Systems for API Testing,” arguing that AI-native testing needs to move beyond faster test generation toward coverage judgment, execution feedback, and continuous maintenance. The whitepaper highlights the limitations of traditional test generation approaches and proposes a new framework for improving software reliability through adaptive AI systems. According to the research, most automated testing systems today rely on static generation methods that struggle to adapt when APIs evolve, business logic changes, or new failure patterns emerge, resulting in gaps in coverage, missed edge cases, and increased software risk. The paper argues that the future of API testing will depend on adaptive coverage systems: AI-driven testing frameworks capable of continuously learning from execution outcomes, correcting mistakes, and refining test strategies over time. The whitepaper introduces key concepts including model orchestration for broader test exploration, AI-powered QA judgment layers, correction feedback loops, execution-driven learning, and adaptive coverage mechanisms. The release builds on KushoAI’s earlier introduction of APIEval-20, an open benchmark for evaluating AI agents on real-world API bug detection.