Coval helps companies simulate thousands of scenarios from a few test cases, allowing them to test their voice and chat agents comprehensively. Built by experts in autonomous testing, Coval offers features like customizable voice simulations, built-in metrics for evaluations, and performance tracking. It is designed for developers and businesses looking to deploy reliable AI agents faster.

Vision Agent is an open-source AI framework that enables developers and QA engineers to automate graphical user interfaces through vision-based element detection and natural-language-driven scripting. It leverages computer vision models to locate buttons, forms, and interactive components on screen, then uses a large language model to translate user instructions into executable automation code. The agent adapts to UI changes, ensuring robust and low-maintenance test suites for web and desktop applications. It offers a Python SDK, CLI tools, and integration with CI pipelines for seamless end-to-end testing workflows.

BaseRock.ai is an innovative QA platform that leverages artificial intelligence to automate unit and integration testing processes. Designed to be user-friendly, it requires zero learning curve, making it easy for developers and QA teams to produce and run test cases with a single click. This platform ensures maximum test coverage, detects bugs early, and provides detailed feedback to boost developer productivity. Additionally, BaseRock.ai integrates seamlessly into CI/CD pipelines, which enables frequent and reliable software deployments.

ToolFuzz provides a comprehensive fuzz testing framework specifically tailored for tool-using AI agents. It systematically generates randomized tool invocation sequences, malformed API inputs, and unexpected parameter combinations to stress-test the agent’s tool-calling modules. Users can define custom fuzz strategies using a modular plugin interface, integrate third-party tools or APIs, and adjust mutation rules to target specific failure modes. The framework collects execution traces, measures code coverage for each component, and highlights unhandled exceptions or logic flaws. With built-in result aggregation and reporting, ToolFuzz accelerates the identification of edge cases, regression issues, and security vulnerabilities, ultimately strengthening the robustness and reliability of AI-driven workflows.