Coval
Simulation & evals for voice and chat agents
Description
Coval provides a specialized platform designed to manage, simulate, and evaluate AI conversational agents. It addresses the challenges of manual testing by enabling users to simulate thousands of scenarios from a limited set of test cases. The system leverages AI to generate diverse test environments and interacts with agents through both text and voice, supporting comprehensive testing for various conversational AI applications.
The platform facilitates detailed performance evaluations using built-in metrics like latency, accuracy, and tool-call effectiveness, along with options for custom metrics tailored to specific business needs. Coval supports the entire agent lifecycle, from development, where it helps track regressions by comparing evaluation results and re-simulating changes, to production, where it offers observability features like logging calls, monitoring live performance, setting alerts for deviations, and analyzing workflows for optimization. Its design emphasizes developer experience with seamless integrations and intuitive workflows, drawing on expertise from autonomous systems testing.
Key Features
- AI-Powered Simulations: Simulate thousands of scenarios from a few test cases.
- Voice AI Compatibility: Test agents via voice calls as well as text interactions.
- Comprehensive Conversation Simulation: Use scenario prompts, transcripts, workflows, or audio inputs for testing.
- Customizable Environments: Tailor simulations with different voices and environmental factors.
- Robust Performance Evaluation: Utilize built-in metrics (latency, accuracy, etc.) or define custom ones.
- Regression Tracking: Compare evaluations, replay interactions (transcripts/audio), re-simulate changes, and set alerts.
- Human-in-the-Loop Labeling: Incorporate human feedback into the evaluation process.
- Production Call Monitoring: Log and evaluate live agent performance in production.
- Configurable Alerting: Set up instant alerts for performance thresholds or off-path behavior.
- Performance Analysis Tools: Review runs and trace workflows to identify optimization opportunities.
- Developer-First Design: Offers seamless integrations and intuitive workflows.
Use Cases
- Automating the testing of AI voice agents.
- Streamlining the evaluation of AI chatbots.
- Ensuring AI agent reliability before deployment.
- Monitoring and evaluating AI agent performance in production.
- Reducing manual effort in AI agent quality assurance.
- Comparing performance across different agent versions.
- Identifying and diagnosing regressions in agent behavior.
- Optimizing AI agent workflows based on performance data.
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