Cyara launched its Agentic CX Trust Platform on November 2. Its AI Trust Observability component is designed to evaluate live agent conversations against customer-defined quality and compliance criteria, with findings linked to the relevant conversation turn and timestamp.
Production conversations become evaluation inputs
The company describes separate compliance gates and quality scores, allowing a policy violation to fail a conversation even when other aspects score well. Material production failures can become regression tests for a subsequent agent release.
Policy gates have their own outcome
That feedback loop could help teams preserve lessons from real interactions as prompts, models and tools change. It also makes the evaluator’s own accuracy important, including how reliably it identifies violations and avoids misclassifying acceptable behaviour.
The scoring system needs validation
Customer validation should measure scoring coverage, false positives, missed violations and whether generated regression tests reproduce the original failure. Those results would establish how effectively the platform supports controlled updates to customer-facing agents.
Image: Cyara / source announcement. Used for source-attributed editorial reporting; rights remain with the owner.
Sources & evidence
Sources describe the announcements and evidence available when this analysis was published. Company claims are attributed; plans and trials are not treated as sustained operation.
