Agent assistance
Agent assistance is useful when guidance arrives in time, cites approved knowledge, and fits the existing agent desktop; test all three in a realistic pilot.
- 01
How should AI summaries map into CRM fields after a customer contact?
Define field meaning, case identity, agent confirmation, ownership, and retry behavior before automating post-contact record updates.
- 02
How do you evaluate AI call summaries and after-call work automation?
Compare summary accuracy, agent editing, CRM completion, and actual time recovered after the conversation ends.
- 03
How do you evaluate whether proactive suggestions are relevant or distracting?
Measure topic fit, interruption cost, repetition, and missed opportunities without confusing suggestion relevance with factual accuracy.
- 04
How do you test agent-assist latency from speech to usable guidance?
Build a timestamped latency trace that distinguishes input capture, processing, delivery, and useful guidance on the agent desktop.
- 05
How should QA teams classify errors in AI-generated call summaries?
Use distinct labels for unsupported details, omissions, attribution, uncertainty, structure, and record delivery to target repairs.
- 06
How do you evaluate knowledge-grounded answers for contact center agents?
Test cited agent-assist answers for source fit, current policies, permissions, ambiguity, and safe behavior when knowledge is missing.
- 07
Who should own knowledge refresh for contact center agent assistance?
Assign document ownership, effective dates, deployment checks, and emergency withdrawal before assisted answers depend on changing policies.
- 08
How should you evaluate real-time agent assist during live calls?
Compare agent guidance by useful timing, answer quality, workflow fit, and a pilot that protects customer outcomes.