Cekura Eval Design
Designs, generates, and refines comprehensive test scenarios and evaluation suites for AI voice and chat agents on Cekura.
Cekura Eval Design provides specialized guidance and workflows for authoring structured evaluation suites, behavioral scenarios, and deterministic conditional action tests for AI voice and chat agents. It inspects agent definitions, knowledge bases, and mock tool data to build grounded test cases spanning regression, IVR/DTMF interactions, compliance verification, and red-team edge cases without hallucinating unsupported agent workflows.
Características Principales
01Automated scenario generation grounded in agent records, knowledge bases, and mock tool configurations
02Pre-test agent verification to ensure test assertions are licensed by actual agent specs
03Red-team test planning to uncover vulnerabilities, policy edge cases, and safety failures
048 GitHub stars
05Dual-mode authoring supporting both behavioral personas and scripted conditional actions
06Voice agent testing support including IVR sequences, DTMF steps, interruptions, and latency checks
Casos de Uso
01Conducting red-team probing and compliance validation on enterprise conversational AI
02Building automated regression test suites for customer support voice agents and IVR systems
03Debugging simulated caller behaviors, speech synthesis formatting, and mock tool responses