Build Agentic Sentiment Analysis
Learn how to analyze closed conversations automatically, assign a sentiment score, generate a short AI summary, and classify chats with useful tags in Tiledesk.
This tutorial explains how to build an Agentic Sentiment Analysis flow in Tiledesk.
The goal is to analyze a conversation when the chat is closed, even if the user does not leave an explicit rating or written feedback.
At the end of the flow, Tiledesk can:
detect whether the transcript is meaningful
assign a sentiment score from 1 to 5
generate a short AI summary of how the conversation was perceived
apply tags such as feedback, suggestion, error report, or feature request
This approach helps teams extract value from conversations that would otherwise end without any structured signal.
What this workflow does
When a chat is closed, the workflow triggers an AI step that reads the conversation transcript.
The AI returns:
NC (No Comment) if the transcript is not useful or does not contain enough meaningful information
a score from 1 to 5 to represent the user’s likely sentiment
a short mini-report that explains the likely outcome of the interaction
a category based on the overall conversation
This makes it possible to monitor conversation quality even when the user does not leave a rating.
Step 1: Identify the chat closing point
Open your flow in Tiledesk Design Studio and identify where the conversation ends.
This can be:
a manual close action
an automatic close after the final message
a close triggered after a period of inactivity
a close after a human handoff is completed
This is the point where the Agentic Sentiment Analysis should start.
Step 2: Trigger a post-chat analysis flow
After the closing point, add a step that starts the sentiment analysis workflow.
The purpose of this step is to run the analysis only after the conversation is finished.
This is useful because:
the AI can evaluate the full transcript
the workflow stays lightweight
the analysis is done only once per conversation
Step 6: Apply tags automatically
If your AI returns a category, use it to classify the conversation automatically and add it as a tag using Add Tagaction.
You can use one of these categories:
product_feedback
missing_features
support_quality_issues
repeated_error_reports
This step turns sentiment analysis into something operational, not just descriptive.
After applying tags automatically, you can go to Tiledesk Analytics and view conversations together with their related tags in a simple way.
Simple prompt example for category classification
To keep this part simple, you can add a prompt that asks the AI to read the full conversation and return only one category.
Use a prompt like this:
With this prompt, the AI acts as a strict classifier and returns only one value that you can save in an attribute or apply directly as a tag.
Why this is useful in Tiledesk
This workflow fits well in Tiledesk because it can be built with a no-code approach and extended over time.
It helps teams:
capture signals from unrated conversations
classify feedback automatically
detect recurring issues
improve support quality
turn closed chats into actionable insight
Instead of letting conversations disappear once they are closed, you can use them to generate structured data for continuous improvement.
Hope it’s helpful.
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In case you have a question, email us at: [email protected]
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