Imagine a universal testing tool that could seamlessly navigate the complexities of any software project, regardless of its programming language, build system, or testing framework. Researchers have developed ExecutionAgent, an AI-powered tool that does just that. It’s like having an expert developer who can automatically set up and run tests for you. Testing software is crucial for ensuring quality and catching bugs, but the process can be a real headache. Different projects have different setups, requiring specific tools and configurations. This makes creating a one-size-fits-all testing solution incredibly difficult. Existing methods either rely on time-consuming manual setups, project-specific continuous integration pipelines, or language-specific scripts with limited capabilities. ExecutionAgent takes a different approach, mimicking how a human developer would tackle the task. This AI agent uses a large language model (LLM) to understand project documentation, search the web for helpful tips, and interact with the system just like a human would. It uses a clever technique called 'meta-prompting' to stay up-to-date with the latest technologies, querying the LLM for best practices and guidelines specific to each project's language and tools. The agent works in two phases. First, it gathers information about the project, including language-specific guidelines, containerization best practices, and any existing CI/CD scripts. Second, it enters a feedback loop where it proposes commands, executes them, summarizes the results, and refines its approach based on the outcome. This iterative process allows ExecutionAgent to handle unexpected errors and adapt to complex project setups. In tests on 50 diverse open-source projects spanning 14 programming languages, ExecutionAgent successfully ran the test suites for 33 projects. It even outperformed existing techniques by a significant margin, achieving results close to manually-validated ground truth. The average execution time was 74 minutes per project, with minimal costs associated with LLM usage. While promising, ExecutionAgent still faces challenges. It sometimes repeats mistakes and can struggle to follow through on certain multi-step commands. However, this research opens exciting new doors for automated software testing. Imagine the time and effort saved by automatically testing code changes, validating AI-generated code, and streamlining the creation of software benchmarks. ExecutionAgent represents a significant step towards a future where testing any software project is as easy as clicking a button.