On 2 November 2026 Cyara, a customer experience assurance company, launched the Agentic CX Trust Platform. Its core feature, AI Trust Observability, scores every live conversation handled by an AI agent for quality and compliance, flags violations as they happen and turns each production failure into a test the agent must pass before its next release.
- Works with 55+ AI agent platforms and every major contact center (CCaaS) provider
- Ships scorecards tied to the EU AI Act, HIPAA, PCI DSS and GDPR
- Every verdict is traced to the exact turn of the conversation, with reasoning and a timestamp
Cyara announced the platform in a Business Wire release on 2 November 2026, and destinationCRM covered the launch the same day. The pitch is simple: companies are putting AI agents in front of customers, but most can only prove those agents behave well by sampling a handful of transcripts or by taking the builder's word for it.
"Enterprises are being asked to trust AI agents on the word of their builders. We would never accept that for financial reporting," Cyara CEO Sushil Kumar said. The launch landed the same day Salesforce agreed to buy voice AI company Cresta, which shows where the market is heading: more conversations handled by agents, and more pressure to show what those agents actually said.
Small and mid-size teams are not Cyara's target buyer, but the problem it names is theirs too. Once a chatbot or AI assistant can update deals, quote prices or promise refunds, somebody has to check its work. Teams that connect a language model to their own CRM, often with a development partner such as YuSMP Group, should plan that checking layer from day one rather than bolt it on after the first bad conversation.
What did Cyara launch?
The Agentic CX Trust Platform brings together three jobs: agentic testing before launch, end-to-end journey monitoring, and observability for both AI and human agents in production. The headline piece is AI Trust Observability, which sits outside the agent and grades it independently of whoever built it.
According to the release, Cyara works with more than 450 enterprises and assures about 350 million customer journeys a year, with monitoring across 420+ carriers in 145+ countries. Demos are available now, and the company plans to show the platform at its CX Unfiltered virtual summit on 8 December 2026.
How does the platform check AI agents?
- Every conversation, not a sample. Manual QA usually reviews a small slice of calls and chats. Cyara says it scores 100 percent of agent interactions.
- Compliance separate from quality. A friendly, helpful answer can still break a rule. The platform keeps compliance gates apart from quality scores so one does not hide the other.
- Evidence, not opinions. Each verdict points to the exact turn in the conversation, with the reasoning and a timestamp, so a manager or auditor can see why it failed.
- Failures become tests. "When agentic AI fails, most teams fix the incident but leave the test gap that let it through, so the failure returns with the next release," said chief product officer Amitha Pulijala. Cyara converts each production failure into a regression test.
What does it mean for small and mid-size teams?
- "It works in the demo" is no longer enough. Buyers of AI agents will start asking vendors how quality is measured after launch. Ask that question before you sign.
- Your CRM is the audit trail. If an agent changes a deal stage or logs a promise to a customer, that record is your evidence. A clean structure, as described in our guide to what a CRM database should contain, makes review possible.
- Regulated data raises the stakes. If your agent touches card payments, health details or EU customers, the rules Cyara maps its scorecards to (PCI DSS, HIPAA, GDPR, the EU AI Act) apply to you whatever the size of your team.
- Integration decides what you can check. Transcripts, CRM updates and ticket outcomes often live in different tools. Our CRM integration guide covers how to connect them so one review shows the full story.
What should you do now?
- List what your AI is allowed to do. Write down which records it can change and which promises it can make. Anything outside that list goes to a person.
- Log every AI action in the CRM. Mark agent-made changes with their own source field so you can filter and review them.
- Review on a schedule. Read a fixed number of AI-handled conversations every week, and score them with the same checklist your reps use.
- Turn mistakes into tests. Each time the agent gets something wrong, save the conversation and re-run it after every prompt or model change.
