Coval

What is Coval?

Coval is a cutting-edge platform designed to streamline the testing, monitoring, and optimization of autonomous AI agents, especially for voice and chat applications. Drawing inspiration from autonomous vehicle testing methodologies, Coval automates the generation of extensive test scenarios from minimal inputs, enabling comprehensive coverage and rapid detection of regressions. Its seamless integration with CI/CD pipelines and real-time performance monitoring empowers developers to build dependable AI assistants with faster iteration cycles and confidence in production quality.

Key Features

• Automated Scenario Simulations

  Generates thousands of realistic test cases from minimal prompts, covering diverse user behaviors, accents, and environmental conditions.

• Seamless CI/CD Integration

  Integrates with development pipelines to automate testing on every code change, providing rapid feedback and regression detection.

• Real-Time Performance Monitoring

  Tracks live production calls with detailed logs, alerts on performance anomalies, and supports human-in-the-loop labeling for quality assurance.

• Customizable Evaluation Metrics

  Supports a range of built-in and user-defined metrics such as latency, accuracy, tool-call effectiveness, and instruction compliance.

• Voice and Text Agent Compatibility

  Supports simulation and evaluation for both voice-based and text-based AI agents with customizable voices and environments.

• Developer-First Design

  Offers intuitive APIs, SDKs, and workflow observability tools to accelerate development and deployment of reliable AI agents.

Use Cases

• AI Agent Reliability Testing : Automate extensive testing of conversational AI agents to ensure consistent and accurate behavior across scenarios.
• Accelerated Development Cycles : Integrate automated evaluations into CI/CD pipelines to quickly identify and fix regressions, speeding up release cycles.
• Production Performance Monitoring : Continuously monitor live AI agent interactions to detect issues early and maintain high-quality user experiences.
• Voice Assistant Optimization : Simulate diverse voice environments and accents to improve robustness and accuracy of voice-based AI assistants.
• Custom Metric Development : Define and apply tailored evaluation metrics aligned with specific business goals and agent functionalities.

FAQs