Cyara Launches Agentic Testing and AI Governance to Close the AI Trust Gap in Customer Service

Agentic AI Testing for Voice and IVR

This system evaluates AI agents by using AI-driven test agents to simulate customer interactions across multiple scenarios. 

By analyzing how the voice AI responds, handles branching logic, and behaves under changing inputs, this platform can compare expected outcomes with actual responses and flags any incorrect handling.  

This process reveals failures that scripted test cases often miss as agentic systems typically adapt and vary in behavior. 

Cyara’s testing approach identifies reliability gaps in AI voice systems before customers experience them to ensure speedy resolutions, reducing the risk of faulty dialogues, inconsistent responses, or failures in adaptive logic.  

Compliance and Bias Modules 

These two modules review transcripts and real-time interactions from AI agents to identify risks, examining what the AI said, what it recommended, and how it interpreted user inputs, flagging issues that might affect fairness, safety, or legal adherence. 

The Compliance module compares AI-generated outputs against internal rules and policies, whilst the Bias module measures variation in outcomes across different customer types or scenarios, identifying patterns that may indicate unfair or skewed outcomes. 

When failures in compliance or biases quickly undermine customer trust, these modules identify risks early, helping organizations prevent inconsistent or harmful outcomes before they reach customers. 

Recommendation Engine for Prompt Design and Test Development

The engine analyzes the AI agent’s purpose, the target journey, and the testing goals, generating prompt structures that encourage specific behaviors and proposes ways to combine fixed scripts with adaptive agent testing.  

This helps teams create test cases that reflect real customer behavior, rather than narrow scripted paths, and can produce better evaluations of how an agent will perform in production. 

When enterprises struggle to test adaptive AI systems as traditional test design depends on predictable outputs, the recommendation engine reduces this barrier, speeding up test creation and improving coverage to reveal weak points in agentic behavior before customers encounter them. 

Creating More Predictable AI-Powered CX

These combined capabilities allow enterprises to access a more reliable route of introducing autonomous AI into customer interactions. 

Whilst agentic systems that adapt, make decisions, and generate responses in real time creates new opportunities for efficiency, this also increases the risk of unpredictable outcomes. 

Cyara’s solution provides continuous validation and governance across these adaptive behaviors, creating visibility into how AI systems operate, respond under varied conditions, and comply with policy and fairness requirements.  

It also supports a controlled transition from human-led or scripted workflows to autonomous ones. This helps protect customer trust while organizations scale AI. 

By ensuring higher predictability, consistent experiences, and accurate information, this solution supports safer and more trustworthy interactions.