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Description

Evalgent serves as a platform dedicated to the testing and evaluation of AI voice agents. The common reasons for failures in production are not due to inadequate technology but stem from the fact that demonstrations typically utilize pristine audio and compliant users, which is not reflective of actual user interactions. By identifying potential failures before they can impact production, Evalgent reduces the time needed for iterations and accelerates the path to revenue for voice agents. THE PROCESS 1. Define: establish authentic scenarios and criteria for success. 2. Run: execute tests that mimic realistic human behavior. 3. Measure: identify successful elements, failures, and operational boundaries. 4. Act: obtain clear, actionable insights for necessary adjustments or deployments. KEY FEATURES 1. Scenarios: create and define test cases based on agent directives. 2. Caller Profiles: emulate real user behaviors, including variations in accents, speech speed, and interruption styles. 3. Metrics: utilize custom LLM-related and telemetry scoring to evaluate every interaction. 4. Evaluations: conduct structured testing campaigns that yield pass/fail outcomes along with improvement suggestions. 5. Reviews: incorporate human oversight for corrections, complete with a comprehensive audit trail. This multifaceted approach ensures that voice agents are thoroughly vetted and ready for the complexities of real-world interactions.

Description

An advanced AI-driven testing solution for web applications that identifies issues prior to user encounters is now available. This intelligent agent not only determines the necessary tests but also generates and maintains them to ensure their relevance over time. You can execute these tests directly from our platform or seamlessly integrate them into your CI/CD workflow. The reliability of end-to-end testing has been a significant concern, as failures can stem from more than just faulty code. Factors like external dependencies, timing discrepancies, unpredictable behaviors, race conditions, and state leaks contribute to the unpredictability of test outcomes. To address these challenges, we are implementing effective strategies to help you save valuable time that would otherwise be spent troubleshooting perfectly functioning code. By enhancing the stability of testing processes, we aim to boost overall software quality and developer confidence.