How to Find Problems in Agent Responses from Dialogs
The Dialogs section is not only for reviewing conversations. Dialogs can help you understand what the AI agent is missing: a knowledge base, a skill, more precise instructions, or a clearer conversation flow. So if the agent responds differently than you expected, this is the best place to start the analysis.
Step 1. Start with the dialog list
First, open the dialog list and look at the overall picture. Even on this screen, you can notice repeated weak replies, similar inquiry topics, and conversations that do not lead to the desired result.
It is especially useful to spot:
- dialogs with similar customer questions;
- conversations that stop at the same stage;
- topics where the agent responds too generally or uncertainly.
Step 2. Use filters to find problem conversations faster
If there are many dialogs, do not review them one by one. Narrow the list with filters and choose the conversations where the problem occurs most often.
Filters are useful when you need to:
- find similar inquiries;
- compare dialogs from the same channel or source;
- quickly review a specific group of conversations after changing agent settings.
Step 3. Look at the last message, channel, and source
At the list level, it is useful to quickly assess three signals: last message, channel, and source. They help you understand exactly where the problem occurred and in what context.
- Last message shows how the conversation ended and whether it got stuck on a generic reply.
- Channel helps show whether the issue repeats in a specific communication method.
- Source indicates where the customer came from and whether the failure is tied to a specific chat entry scenario.
Additional signal: unread dialog
If you notice an indicator for a new or unread dialog in the interface, use it as a quick guide. Such dialogs are convenient to check first so you can find fresh agent response errors and recurring failures faster.
Step 4. Open the dialog and review the conversation flow
After the list, open a specific dialog and see exactly how the agent handled the conversation. It is important not only to see the weak reply, but also to understand why it happened.
Check:
- whether the AI agent understood the customer's question;
- whether the reply was clear and to the point;
- whether it is too vague;
- whether it asks unnecessary follow-up questions;
- whether the conversation was brought to the needed result.
For example, if the customer wanted to book an appointment but the conversation ended with only general replies and no next step, that is already a useful signal for adjusting the agent.
How to tell that the knowledge base is lacking
Usually, the following signs indicate a missing knowledge base:
- the AI agent replies too vaguely;
- does not know specific conditions;
- cannot confidently answer about prices, timelines, rules, or restrictions;
- gives very different answers to similar questions;
- the same question is repeated, but the materials clearly lack the needed information.
What to do:
- add the missing document to the knowledge base;
- move the recurring question into FAQ;
- split overly large material into several clear parts;
- check whether the correct knowledge base is connected to the agent.
How to tell that a skill is missing
The following signs usually indicate a missing skill:
- the AI agent answers in words, but does not help move to action;
- the customer wants to leave a request, but the agent does not collect data;
- the customer wants to book, but the agent does not lead the conversation to booking;
- the customer asks about a product, but the agent does not help with selection, cart, or order.
What to do:
- check whether the needed skill has been added;
- make sure the skill is not only added, but also configured;
- check whether the system itself is ready for this scenario, for example online booking or a product catalog.
How to tell that the issue is in the instructions
Sometimes the AI agent answers generally correctly, but not in the way your business needs.
This is usually visible in these signs:
- the reply is formally correct, but sounds too dry;
- the reply is too long;
- the reply emphasizes the wrong thing;
- the agent speaks in a tone you do not expect.
What to check:
- communication style;
- agent instructions;
- how clearly the communication rules are defined.
Look for recurring signals
One weak reply does not always mean a systemic issue. It is much more useful to notice repetition:
- customers often ask the same question;
- the agent regularly gives vague answers on the same topic;
- the agent keeps failing to lead the conversation to a request or booking;
- answers differ greatly in similar dialogs;
- customers often get stuck at the beginning of the conversation.
Such repetitions almost always point not to a random mistake, but to a problem in settings, the knowledge base, or skills.
What to do after analyzing dialogs
After reviewing dialogs, one of four actions is usually enough:
- if facts are missing — update the knowledge base;
- if action is missing — add or configure the needed skill;
- if the right tone is missing — refine the instructions and communication style;
- if it is hard for customers to start a conversation — improve the chat first screen or starter prompts.
The main goal of dialog analysis is not just to notice the problem, but to understand exactly what needs to be changed next.
Quick check scheme
- The agent gives vague answers — check the knowledge base.
- The agent does not lead to action — check the skills.
- The agent uses the wrong tone — check the instructions and style.
- Customers keep asking the same thing again and again — add FAQ or new material to the knowledge base.
What is important not to confuse
A weak reply is not always a model problem. Often the reason is that the agent lacks knowledge, a skill, or more precise instructions.
Reviewing a dialog is not just reading a conversation, but a way to understand how to improve the agent's performance.
If the same failure repeats, it should be fixed at the level of settings, the knowledge base, or skills.