# AI Condition

**AI Condition** is an intelligent action that lets you automate decision-making inside a conversation flow using **Generative AI models (LLMs)** such as **OpenAI GPT-5**, **Google Gemini**, **Anthropic Claude**, **Grok**, and more.

<figure><img src="https://1328774413-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FVguk6y5OMn3pAaHZSiwU%2Fuploads%2FhduWTfZdj015t8rj470h%2Fimage.png?alt=media&#x26;token=6461c8b2-e1a9-40ef-9789-e5d7524cce3a" alt=""><figcaption></figcaption></figure>

Unlike the standard Condition Action, which relies on basic logic (for example, “if value equals…”), the **AI Condition** uses language understanding to evaluate the semantic meaning of the user’s message and make context-aware decisions.

{% hint style="info" %}
Tiledesk can only continue down one Condition path per execution.

If you define a Condition and do not add a step afterwards, the execution will stop.
{% endhint %}

## Why use AI Condition

This action is ideal when your logic can’t be expressed through simple rules. It allows you to:

* Interpret tone, intent, or context directly from natural language.
* Route conversations automatically to the correct department or human agent (Sales, Support, Billing, etc.).
* Improve First Contact Resolution (FCR) by connecting users to the right resource on the first try.
* Detect frustration or dissatisfaction and immediately transfer the chat to a live operator.
* Apply complex decision logic without writing code or creating multiple manual conditions.

## Where to find it

In the left panel of the Design Studio, go to **AI → AI Condition**. This action is currently marked as **BETA**.

## How it works

{% stepper %}
{% step %}

### Step 1

Sends the user’s latest message to the selected LLM (for example, GPT-5 or Gemini).

<figure><img src="https://1328774413-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FVguk6y5OMn3pAaHZSiwU%2Fuploads%2FnQMjyKVDntM2GVy6Xvf2%2FUntitled.001.png?alt=media&#x26;token=3edcf156-037a-4c1e-b7f1-d985c3aeb045" alt=""><figcaption></figcaption></figure>
{% endstep %}

{% step %}

### Step 2

The model analyzes the language and evaluates whether one or more **AI Conditions** are met.
{% endstep %}

{% step %}

### Step 3

Based on the result, the flow continues along the corresponding **Success** paths (for example, Support, Sales, Transfer to Human).
{% endstep %}

{% step %}

### Step 4

If the model can’t classify the message, the **Fallback** connector is triggered.
{% endstep %}
{% endstepper %}

### Example 1: Detecting frustration and transferring to a human

You can use **AI Condition** to enhance the way you handle difficult or emotional conversations. For instance, if a user shows frustration or dissatisfaction, the chat can be automatically escalated to a live agent.

#### How to configure

{% stepper %}
{% step %}
Drag an **AI Condition** block into your flow.
{% endstep %}

{% step %}
Select the LLM (for example, OpenAI GPT-5).
{% endstep %}

{% step %}
In Condition 1, enter:

> If the user shows any sign of frustration, dissatisfaction, or emotional distress, whether expressed directly or implied through tone, wording, or repeated complaints.
> {% endstep %}

{% step %}
Set the **Success** path to **Transfer to Human**.
{% endstep %}

{% step %}
In the *Instructions* field, type: `User said: {{lastUserText}}`

<figure><img src="https://1328774413-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FVguk6y5OMn3pAaHZSiwU%2Fuploads%2Fn1fHWoHyJk6g37U6DLHl%2FGuide%20Articles%20(15).png?alt=media&#x26;token=d707c8d8-7d0b-4f90-b2b4-8dd69a87f2f6" alt=""><figcaption></figcaption></figure>
{% endstep %}
{% endstepper %}

Benefit: Proactively detect unhappy users and prevent churn by connecting them with a human representative at the right moment.

### Example 2: Routing between Support and Sales

With multiple AI conditions, you can design a smart routing system that identifies the right department based on the user’s query.

#### How to configure

{% stepper %}
{% step %}
Add an **AI Condition** block and select a model (for example, OpenAI GPT-5).
{% endstep %}

{% step %}
Define two conditions:

* Condition 1:

  > If the user’s last question is related to support, troubleshooting, instructions, tutorials, or guidance on how to perform a task\
  > Success → **Support dept**
* Condition 2:

  > If the user’s question is related to subscriptions, pricing, plans, upgrades, discounts, or any other sales-related topic\
  > Success → **Sales dept**
  > {% endstep %}

{% step %}
In the *Instructions* field, type: `User’s last question: {{lastUserText}}`

<figure><img src="https://1328774413-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FVguk6y5OMn3pAaHZSiwU%2Fuploads%2FvuR7Srzalx9g5ZBmvGhT%2FGuide%20Articles%20(14).png?alt=media&#x26;token=d8419d82-cc64-4d8e-991d-99637e5de158" alt=""><figcaption></figcaption></figure>
{% endstep %}
{% endstepper %}

Benefit: Automatically send each conversation to the right team without human intervention, reducing waiting times and increasing First Contact Resolution (FCR).

### In summary

**AI Condition** takes conversational automation to the next level — transforming the natural-language understanding of large language models into actionable routing decisions that improve efficiency and customer satisfaction.

It’s the foundation for building **human-AI hybrid flows** that are more adaptive, empathetic, and productive.


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