AI-Powered Agent Development
Client
Coaching & LMS
Year
2023-2024
Closing the loop from audited calls to structured agent growth.
Scope of Work
40%
less manager coaching time
4.5 days
faster agent onboarding
70%+
course completion
CSAT ↑
for coached agents
The problem
Rule Engine caught what agents were doing wrong. Live Agent Assist nudged them in the moment. But neither helped an agent improve over time — coaching remained manual, inconsistent, and dependent on manager bandwidth.
Convin’s early LMS had low completion, under 30%, and coaching that did not map back to what calls revealed agents needed help with.
The insight: sequence matters
Research showed the LMS was failing for a structural reason, not a content reason. It taught concepts after showing agents their mistakes, when the more effective order was concept first, mistake second. That sequencing change became the backbone of the redesign.
What didn’t work
Two early bets did not survive contact with real agents. Placing mock calls too early interrupted learning momentum. TTS voice for training scenarios undermined how seriously agents took the material, so we moved to real recorded call snippets. Both were cut rather than shipped half-working.
Results
Once the sequence and mock-call placement were corrected, completion rose from under 30% to more than 70%. Coaching time dropped by roughly 40%, and new agents ramped about 4.5 days faster.
What I’d take forward
The order information arrives in matters as much as the information itself. Cutting a feature that is not working is a design decision, not a failure.
“Seeing the correct approach before my own error made it land, rather than feeling like criticism.”
Test participant, during research
Product Designer, Learning Experience & Research