Here’s how I approach it:

• I always filter first. Raw data can be messy, so I use filters like “Completed + Human = No” if I want to see how it handles voicemails, or “Completed + Appointment = Yes” when I want to check booking flows. That way I’m only looking at the calls that matter.

• Transcripts are gold. Reading a couple of transcripts per day shows me if the agent is following the prompt. For example, I once saw it skipping over user responses too quickly—easy fix by adding more in the prompt.

• Recordings tell another story. Transcripts don’t show tone or pacing. When I noticed the greeting sounded clipped, I just increased the welcome delay from 800 to 1200 ms and the problem disappeared.

• Extracted data = real performance. If I expect emails, names, or appointment dates and the fields are blank, I know the prompt isn’t guiding the AI enough. Adding phrases like “let me spell that back to you” made the capture rate much better.

• Exports help with big picture. Once a week, I export the CSV and look at average call duration and completion rate. Short calls often mean hang-ups or poor prompts; long calls show me where the agent is stuck.

Honestly, before I started digging into Call History, I thought my agent was doing “fine.” After two weeks of reviewing transcripts and exports, performance clearly improved: better appointment rates and fewer confused users.

So if you want to know how your agent is really doing, don’t just look at whether it answers calls—spend time in Call History. It’s the best tool to turn a decent agent into a great one.