Beyond the Essentials: Deeper Signals That Reveal How Your AI Agents Perform in the Real World
Once you’ve mastered the six core metrics—Instruction Following, CSAT, Hallucination Rate, Interruption Handling, Latency, and Voice Quality (as covered in our previous blog)—it’s time to expand your monitoring with more specialized signals. These supporting metrics don’t replace the essentials, but they provide richer insight into user experience, conversation dynamics, and technical performance.
12 Supporting Metrics for AI Conversation Monitoring
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User Interrupting AI
When users cut the AI off mid-response, it often signals impatience or dissatisfaction. High interruption rates may mean answers are too long, off-topic, or poorly timed. Tracking this helps you refine pacing and conversational flow. -
Words per Minute (WPM)
The speed of speech impacts both comprehension and naturalness. An AI speaking too fast feels rushed, while too slow feels awkward. Monitoring WPM ensures that delivery matches user comfort levels. -
Not Early Termination
Premature call or chat endings can reflect frustration or technical breakdowns. Measuring “not early termination” ensures conversations reach their intended outcome rather than being abandoned. -
Response Consistency
Users expect similar questions to yield similar answers. Inconsistencies undermine trust. Measuring this keeps responses predictable across sessions. -
Sentiment
Beyond outcomes, how users feel during interactions matters. Tracking sentiment across the exchange reveals frustration, delight, or confusion that raw success rates can’t capture. -
Talk Ratio
The balance between AI speaking and user speaking should feel conversational. If the AI dominates, users may disengage; if users do all the talking, the system may not be guiding effectively. Talk ratio helps measure this balance. -
Average Pitch (Hz)
Voice agents should sound natural and approachable. Monitoring pitch variation avoids monotone delivery and ensures the AI voice remains pleasant and engaging. -
Infrastructure Issues
Even the best models fail if infrastructure falters. Tracking errors like dropped calls, failed connections, or API timeouts ensures you can separate technical problems from model issues. -
AI Interrupting User
Sometimes the AI itself cuts users off, either due to poor barge-in handling or latency. This frustrates users and breaks flow. Measuring this metric helps tune interruption thresholds and improve turn-taking. -
Relevancy
Answers should stay focused on the user’s request. Off-topic or filler responses reduce efficiency and satisfaction. Measuring relevancy ensures conversations remain useful and goal-driven. -
Stop Time After User Interruption (ms)
When a user interrupts, the AI should stop quickly and gracefully. Slow stop times make it feel unresponsive. Monitoring this reaction time helps create a more natural back-and-forth flow. -
Unnecessary Repetition Count
Repeating the same phrases or questions makes the AI feel robotic and wastes user time. Tracking repetition counts helps teams tune prompts and reduce redundancy.
How Cekura Brings It All Together
While the six essentials ensure agents are fundamentally reliable and trustworthy, these supporting metrics add depth. They capture how natural the conversation feels, how smooth the technical delivery is, and how engaged users remain from start to finish. Together, they paint a more complete picture of agent performance.
Tracking just one or two metrics isn’t enough—-and managing dozens of disconnected dashboards quickly becomes overwhelming. That’s why Cekura combines the 6 essential metrics with these 12 supporting ones into a unified monitoring framework.
- Essentials first: Instruction Following, CSAT, Hallucination Rate, Interruption Handling, Latency, and Voice Quality.
- Supporting next: Flow, tone, technical delivery, and engagement metrics like interruption handling, WPM, talk ratio, latency, relevancy, and more.