This article explores the case of the usage of artificial intelligence (AI) for optimizing the process of covering distributed systems with consumer-driven contract test, analyzing the pros and cons of this approach. Considering the complexity of development of modern distributed systems, like microservices, and the need to ensure the system components interactions keep reliable as long as the system keeps evolving this study is focused on finding the most effective way to introduce the contact testing into such systems to maximize the contracts tests coverage while minimizing development costs. The contract testing has its challenges: steep learning curve, impact on the delivery lifecycle, spreading the approach consistently across the organization. These challenges often lead to teams sacrificing the benefits of the approach and using more traditional ways of testing, like end-to-end (E2E) testing, which however does not fit well into distrusted system. The described methodology includes generating (by AI platform) the contract between the parties (consumer and provider), generating the consumer test to verify the provider is compatible with the expectations the consumer has of
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