Testing of web APIs is nowadays more critical than ever before, as they are the current standard for software integration. A bug in an organization’s web API could have a huge impact both internally (services relying on that API) and externally (third-party applications and end users). Most existing tools and testing approaches require writing tests or instrumenting the system under test (SUT). The main aim of this dissertation is to take web API testing to an unprecedented level of automation and thoroughness. To this end, we plan to apply artificial intelligence (AI) techniques for the autonomous detection of software failures. Specifically, the idea is to develop intelligent programs (we call them “bots”) capable of generating hundreds, thousands or even millions of test inputs and to evaluate whether the test outputs are correct based on: 1) patterns learned from previous executions of the SUT; and 2) knowledge gained from analyzing thousands of similar programs. Evaluation results of our initial prototype are promising, with bugs being automatically detected in some real-world APIs.