> ## Documentation Index
> Fetch the complete documentation index at: https://explore.airia.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Loop over CSV

The CSV Loop feature enables your agent to process data systematically by iterating through each row or column of a CSV file. **Each iteration triggers a separate agent execution**, making it ideal for batch processing operations.

This powerful capability is especially valuable for:

* Data cleaning and standardization
* Large-scale content generation
* Data enrichment and validation
* Automated analysis across multiple records

## Set Up CSV Looping

Follow these steps to configure and use the CSV Loop feature:

1. **Navigate to Behavior Settings:**
   In **Agent Studio**, go to **Behavior**.
2. **Enable CSV Loop:**\
   Toggle on the **Loop over CSV** option.

   <Note>
     Enabling this feature activates the CSV upload functionality in the **Playground**.
   </Note>
3. **Upload Your CSV:**
   In the **Playground**, you will now see a CSV upload option. Upload your desired CSV file.
4. **Configure Iteration:**
   Select whether you want the agent to loop over **rows** or **columns**.
5. **Start the Loop:**
   Configure your agent's message or prompt as needed. Send the message in the chat to initiate the loop operation.

## Monitor Loop Progress

You can track the progress of your CSV loop as it runs:

* **Real-time Tracking:** Monitor loop progress directly in the **Playground** chat interface while it's active.
* **Session Limitation:**

  <Warning>
    If you close the **Playground** and return later, the real-time loop progress will not be visible in the chat history.
  </Warning>
* **Completion Notification:** Once the loop completes, you'll receive a summary notification in the **Notification Center**.

## Common Use Cases

The CSV Loop is powerful for various tasks, including:

### Data Cleaning and Standardization

**Scenario:** Process a CSV file containing inconsistent or incomplete data.

**Input Data Issues Examples:**

* Inconsistent address formats
* Missing company websites
* Varied job title formats
* Incomplete contact information

**Agent Actions:**

* **Address Processing:** Parse and reformat addresses into a standard structure (e.g., Street, City, State, Zip).
* **Phone Number Standardization:** Convert phone numbers to a consistent format.
* **Website Enrichment:** If **CompanyName** exists but **Website** is missing, search the web for the official company website.
* **Industry Classification:** Infer **Industry** or **CompanySize** categories based on **JobTitle** and **CompanyName**.

**Output:** A cleaned and enriched dataset with standardized information.

### Product Description Generation

**Scenario:** Create unique product descriptions from attribute data.

**Input Data Examples:**

* `ProductName`
* `Features` (list or descriptive text)
* `Material specifications`
* `TargetAudience` demographics
* `ProductCategory`

**Agent Actions:**

* **Feature Analysis:** Analyze product features and benefits.
* **Audience Targeting:** Tailor language and messaging to the specified target audience.
* **Description Generation:** Create compelling product descriptions that highlight benefits derived from features.
* **SEO Optimization:** Generate meta descriptions and relevant keywords.
* **Format Optimization:** Structure content with bullet points for key features and benefits.

**Output:** Professional product descriptions ready for e-commerce platforms or marketing materials.

## Best Practices

* **Data Preparation:** Ensure your CSV file is properly formatted with clear column headers.
* **Agent Configuration:** Test your agent's behavior on a small sample of data before processing large datasets.
* **Progress Monitoring:** Keep the **Playground** open during execution to monitor real-time progress.
* **Result Validation:** Review the completion summary and validate the outputs as needed.