AI agent analysis
Conversational AI quality assurance: check real chats
Conversational AI quality assurance after launch: check what your AI agent really said to customers, what to look for, and what a review cannot tell you.
Conversational AI quality assurance after launch means reading what your AI agent really said to customers. Do not only run your test questions again. Read the real conversations, both sides. Check each one against a short list of rules. Save the exact words behind every problem. Then let a person decide what to fix.
Your tests cover the questions you thought of. Your customers ask all the others.
Most guides on this topic are about testing before launch. You write test questions, run pretend chats and check the answers. That is useful. But it stops on launch day, which is when your AI agent starts talking to people you cannot predict.
Key takeaways
- Conversational AI quality assurance after launch means reading what your AI agent really said to customers.
- NIST says real-world use can reveal problems that might not surface in controlled testing.
- Check each conversation against a short list of rules, and save the exact words behind every problem.
- A flagged reply is a possible problem, not proof. A person decides what to fix.
What is conversational AI quality assurance?
An AI agent is software that talks to your customers, in chat or on the phone. Conversational AI is another name for it.
Quality assurance, or QA, means checking its work against a standard. Are its answers right, safe and useful? There are two ways to check:
- Testing: ask it questions you wrote.
- Review: read the conversations it really had.
If you bought your AI agent from a vendor, review is the part you control. You may not see how it works inside. You can always read what it said.
Why is testing before launch not enough?
Because real customers ask odd things. The US standards agency, the National Institute of Standards and Technology (NIST), says the same. Its guidance on generative AI tells organizations to "evaluate GAI system performance in real-world scenarios" to "reveal issues that might not surface in controlled and optimized testing environments." GAI is its short name for generative AI, the kind behind most new AI agents.
NIST also names a risk many owners worry about. It calls a confident but false answer a "confabulation", and asks for monitoring after launch, "particularly for potential confabulation."
Here is what that looks like. A test can show that your AI agent knows your refund policy. Only a real conversation shows it making up a new policy for a customer who asked in an odd way.
What goes in a conversational AI quality assurance checklist?
Keep your rules short and write them in plain sentences, as you would for a new team member. These conversational AI quality assurance best practices work for any team. Here is a starting checklist:
| Check | What a problem looks like |
|---|---|
| Right answer | It gives a price, policy or date that is wrong |
| Stays on its job | It gives advice it should not give |
| Hands over to a person | The customer asks for a person, or asks the same thing twice, and the AI agent carries on |
| Says it is an AI | The customer seems to think they are talking to a human |
| Gets the job done | The chat ends and the question is still open |
| Right tone | The words are correct but the moment is wrong, like a cheerful reply to an angry customer |
The "says it is an AI" row is more than good manners. If your AI agent talks to people in the EU, the EU AI Act, Article 50, says the provider must make sure those people are "informed that they are interacting with an AI system", unless that is obvious from the context. Ask your vendor how their AI agent does this, and check it in your own conversations.
How do you run a conversational AI QA process on real conversations?
You do not need engineers. Here is a simple conversational AI quality assurance process:
- Get the conversations. Ask your vendor for the full logs, both sides, with dates.
- Write your rules down. One sentence per check, with an example of a good reply and a bad one.
- Read all of them, not just a few. A random handful finds the common problems and misses the rare one that loses you a customer. Phone teams know this from manual call QA sampling.
- Save the exact words. For each problem, keep the customer's message and the AI agent's reply. "The AI agent was rude" starts an argument. The exact words end it. For example, a customer writes "I was charged twice, can you refund one?" and the AI agent replies "We do not offer refunds on that plan", but your policy says you do. Those two lines are the whole finding.
- Let a person decide. A flagged reply is a possible problem, not proof. Someone who knows your policy checks it before anything changes.
- Check again after each fix. Make sure the fix worked and that nearby answers still read right.
Reading everything takes a lot of hours. That is the part worth automating, as long as every flag keeps the exact words behind it.
What can't a review of conversations tell you?
A review shows what the AI agent said. It does not always show what happened next. The customer may have phoned you, found the answer somewhere else, or given up. Find out what happened next before you call a chat a failure.
A review also only sees the chat in front of it. That is why a flagged reply is a possible problem until a person who knows your policy has checked it.
Where Metricsense fits
You can do the six steps above by hand on last week's chats. The hard part is reading every conversation, every week. That is the part Metricsense takes over.
You upload the chat logs from your AI agent. Metricsense reads each conversation, both sides, and checks it against the rules you write in plain English. There is no tagging and no model training. Every finding links to the exact message behind it, so a person can check it and decide what to fix.
Want to see it on your own conversations? A pilot runs on around 50 of your calls, with a readout inside a week. Book a free pilot on your calls.
