Customer Support Agent Lab

A $750 lab for designing customer support AI agents with retrieval, escalation rules, QA scorecards, safety boundaries, and human-in-the-loop operations. Covers intake triage, knowledge grounding, reply drafting, handoff logic, and support analytics. Built for teams that want agents to reduce support load without damaging customer trust. Core sources: - https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf - https://platform.openai.com/docs/guides/tools - https://arxiv.org/abs/2210.03629

Curriculum

1.
Support agent scope
Separate answer drafting, ticket routing, self-service, refund requests, account actions, and human escalation.
2.
Knowledge base readiness
Assess source freshness, ownership, conflicting articles, policy exceptions, and citation requirements.
3.
Escalation design
Rules for refunds, billing, legal threats, angry customers, identity issues, and low-confidence answers.
4.
Conversation QA scorecards
Evaluate helpfulness, policy accuracy, tone, citation quality, resolution, and escalation judgment.
5.
Agent sandbox testing
Build test tickets for common questions, edge cases, adversarial prompts, missing data, and policy conflicts.
6.
Launch operations
Roll out behind agents, measure review load, and tighten the system before direct customer exposure.
7.
Support analytics loop
Turn failures into article updates, prompt changes, routing rules, and product feedback.