Goal-driven conversation tests
Describe what the customer is trying to achieve instead of hard-coding every turn. The simulated user can adapt to the live agent response.
- Customer goals
- Personas and playbooks
- Recovery paths
Test conversational AI across realistic multi-turn journeys, semantic quality, business rules, execution integrity and release evidence.
Conversational AI can take multiple valid paths to the same customer goal. Testing therefore needs to evaluate outcomes and constraints while allowing runtime variation in wording and route.
Describe what the customer is trying to achieve instead of hard-coding every turn. The simulated user can adapt to the live agent response.
Evaluate the complete interaction, not isolated messages. Preserve conversation context and the evidence behind each important judgment.
Use deterministic assertions for requirements that must be exact, including required handoffs, authorization and business state changes.
Apply the same assurance model to conversational and voice workflows where the system has to achieve a goal through an adaptive interaction.
Define a business goal and persona, execute a realistic multi-turn journey, evaluate semantic and deterministic behaviour, verify execution integrity and retain the evidence behind the verdict.
Yes. Goal-driven testing can allow valid wording and path variation while enforcing deterministic business constraints and the intended outcome.
Yes. Vera can exercise conversational and voice agents through realistic customer-style journeys.
Bring one business journey and see how Shyena maps, tests, evaluates, secures and connects the evidence to release decisions.
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