FDE Interview Questions: Customer Service AI Agent
Examine common interview follow-up questions about the design of an AI customer support agent. Define success metrics beyond customer satisfaction ratings, identify workflow failures, and distinguish what each processing checkpoint confirms. Measure end-to-end latency and operational costs using representative workloads, and communicate release decisions to support, compliance, and executive stakeholders.
We'll cover the following...
- What would make 98 favorable answers a success
- Evidence that detects the six failed workflows
- What R-204 can truthfully claim at each checkpoint
- What changes under stale policy, abuse, or tool failure
- Measuring eight seconds and USD 12 honestly
- Practice: A release decision that survives the follow-up
- FDE role-specific questions
LumenPay's release dashboard reports that 98 out of 100 support conversations were rated as accurate. That score alone is insufficient for release approval. An accurate response may still result in duplicate case creation or a handoff without an assigned owner. Support each customer-facing claim with evidence from the authoritative system, and verify that failure scenarios preserve access controls and workflow guarantees.
What would make 98 favorable answers a success
The release review contains three workflows:
Status reads: 40 tasks; all 40 produce authorized, bounded answers matching payment-service evidence at its observation time.
Policy explanations: 30 tasks; 29 use applicable published policy. One uses a superseded policy.
Refund intake: 30 tasks; only 25 produce exactly one recoverable case, truthful handling state, and verified receipt by the responsible human owner. Three lack owner receipt; two create duplicate cases.
The verified workflow score is therefore (40 + 29 + 25) / 100 = 94%. Keep the per-workflow results visible: The intake result is 25 / 30, approximately 83.3%, even though every status read passes.
The tempting dashboard labels all 30 case-creation responses “automated resolutions.” That denominator rewards attempted intake regardless of its outcome. Version one permits policy explanations, authorized payment-status reads, and refund-request intake; a successful intake or handoff establishes no completed refund and permits no payment mutation.
Workflow success requires authoritative outcome evidence and safe behavior throughout the execution trace. Favorable wording remains useful evidence about communication, but it cannot establish ownership, authorization, or ...