> For the complete documentation index, see [llms.txt](https://docs.uptiq.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.uptiq.ai/platform-resources/skill-library/ai-skills/intent-classification.md).

# Intent Classification

**Intent Classification** takes a natural-language query and matches it to one of the intents you've defined, so the workflow can route to the right action rather than guessing.

{% hint style="info" %}
**Reference layer pending.** This page documents the skill's purpose and configuration as used in the builder. Its formal configuration schema, output schema, and error codes are not yet documented — see the [Skill Library](/platform-resources/skill-library.md) overview.
{% endhint %}

## Worked example: routing inquiries in a financial chatbot

An agent powers a financial inquiry chatbot. A customer types *"Can you please check my account balance?"* or *"I need to dispute a recent transaction."* The workflow has to understand which, and route accordingly.

**The problem.** Customers phrase the same goal in different ways. A keyword search misses *"How much money is in my account?"* — no keyword matches, but the intent is obvious to a person.

**The approach.** Define a set of intents (`CheckAccountBalance`, `DisputeTransaction`) with examples of how each might be phrased. The skill uses an LLM to match the query to the best-fitting intent.

**1. Choose the model.** Select a model from the **Model ID** dropdown.

<figure><img src="/files/SG9cvEVrKS5O2kBhthla" alt=""><figcaption><p>Choosing the model for Intent Classification</p></figcaption></figure>

**2. Define your intents.** This is the part that matters. For each:

* **Name** — a clear identifier, e.g. `CheckAccountBalance`.
* **Description** — what the intent means: *"Triggered when a user wants to know their current account balance."*
* **Examples** — several phrasings a user might actually use: *What's my balance?*, *How much money is in my account?*, *Show me my current funds.* These are what teach the model to recognize the intent.

Repeat for every intent the workflow handles — `DisputeTransaction`, `ApplyForLoan`, and so on.

**3. Set the query.** Point the **Query** field at the user's message from the previous step — `$input.userQueryText`.

<figure><img src="/files/yANRMf07w3v8OIUzyrRn" alt=""><figcaption><p>Feeding the user's message in for analysis</p></figcaption></figure>

{% hint style="info" %}
The quality of the examples decides the quality of the classification. Write the phrasings your customers actually use — including the awkward ones — rather than the tidy version you'd write yourself.
{% endhint %}

## Output

* **`intentMatched`** — `true` or `false`: whether a suitable intent was found.
* **`intents`** — the list of potential matches. **The first item is the best match.** Branch on this to route the workflow.
* **`query`** — the original query that was analyzed.

Handle `intentMatched: false` deliberately. A user whose intent you didn't anticipate is a real case, and falling through to nothing is a worse experience than saying you didn't understand.

***

To add this skill to an agent, see [Adding a Skill to the Agent](/agent-builder/build/adding-a-skill-to-the-agent.md).


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