AI agents
Begin virtual-agent evaluation with bounded tasks, clear action permissions, recovery paths, and human handoffs; expand scope only after realistic exception testing.
- 01
What end-to-end CX automation needs to prove
Define workflow boundaries, authoritative actions, recovery, and accountable handoffs before buying end-to-end automation.
- 02
How to test virtual-agent recovery when a connected system fails
Design timeout, retry, uncertain-state, and escalation tests that keep AI-agent recovery from repeating or misreporting actions.
- 03
How to design a virtual-agent handoff that preserves the customer task
Specify the destination, context packet, acknowledgment, and fallback needed when a contact center AI agent hands over to a person.
- 04
How to test interruptions and corrections in a voice AI agent
Build a turn-taking test matrix covering speech interruption, corrected details, false starts, and action cancellation.
- 05
How to set knowledge boundaries for a support virtual agent
Specify which sources an AI support agent may answer from and test missing, conflicting, stale, and unsupported information.
- 06
How to define task success for contact center virtual agents
Separate completed tasks, correct handoffs, abandonment, and unknown outcomes when measuring AI-agent performance.
- 07
How to evaluate a voice AI agent beyond its demo
Test turn-taking, recovery, task completion, and human escalation before choosing a contact-center voice agent.
- 08
How to run a language-specific voice AI pilot
Evaluate voice AI by language, locale, task vocabulary, call conditions, and fallback instead of relying on a language-count claim.
- 09
Choosing AI agents for both voice and chat
Evaluate voice and chat AI through separate channel tests and a shared task, knowledge, and escalation model.