Conversational AI evaluation is not one metric. A system can sound fluent and still fail the user’s task, call the wrong tool, leak private data, or take too long to respond.
Business Metrics
Start with the user outcome:
- Task completion.
- First-contact resolution.
- Escalation rate.
- Conversion or booking rate.
- Deflection quality.
- User satisfaction.
- Cost per successful conversation.
These metrics tell you whether the system is worth deploying.
Conversation Metrics
Conversation-level metrics explain the experience:
- Clarification rate.
- Repetition rate.
- Abandonment rate.
- Human handoff rate.
- Correction rate.
- Unsafe or off-policy response rate.
For support and sales, review full scenarios rather than isolated turns.
Tool Metrics
Tool calls are where AI systems touch reality.
Track:
- Tool selection accuracy.
- Argument validation failures.
- Tool timeout rate.
- Retry rate.
- Write-action confirmation rate.
- Permission denial rate.
- Recovery success after tool failure.
Never let the model be the only permission boundary.
RAG Metrics
Evaluate retrieval before generation:
- Was the right document retrieved?
- Was the retrieved chunk enough to answer?
- Were citations correct?
- Was access control enforced?
- Did stale content appear?
- Did the model answer “not found” when needed?
Voice Metrics
Voice agents need timing metrics:
- End-to-end latency.
- End-of-turn delay.
- STT finalization time.
- LLM time to first token.
- TTS time to first byte.
- Barge-in response time.
- False interruption rate.
- Long silence rate.
Good voice UX is a timing problem as much as a language problem.
Safety Metrics
Track:
- Policy violations.
- Incorrect refusals.
- PII leakage.
- Missing AI disclosure.
- Missing recording consent.
- Unsafe tool-use attempts.
- High-stakes escalation failures.
Benchmark Principle
A leaderboard is only useful when the methodology is public. For conversational AI, methodology should include scenarios, user personas, tool definitions, channel conditions, language coverage, latency measurement points, and scoring rubrics.
Until then, a transparent directory is more honest than a fake score.