Multimodal AI Agent for Online Surveys: Drag’n Survey’s Complete 2025 Guide

What is a Multimodal AI Agent for Survey Creation?

A multimodal AI agent is a conversational artificial intelligence capable of interacting with your surveys throughout their lifecycle: creation from PDF, Word, PowerPoint files, images and URLs, editing through natural dialogue, and conversational analysis of results. Unlike conventional AI that is limited to generating a survey, Drag’n Survey’s AI agent ecosystem supports you from start to finish.

>> Visit Drag’n Survey to create an AI survey, click here

Drag’n Survey, software used in 185 countries with over 500,000 users, has revolutionised survey management with the gradual launch of its AI agents. Following the creation agent, the editing and results analysis agents now complete the ecosystem for an experience entirely driven by artificial intelligence.

The 3 Drag’n Survey AI Agents

Drag’n Survey’s AI agent ecosystem covers the entire survey lifecycle. Each agent specialises in a specific phase, enabling natural and efficient interaction at every stage.

1. Multimodal Creation Agent

The creation agent allows you to generate complex surveys from multiple sources: PDF, Word, PowerPoint files, images, website URLs and text instructions. It dialogues with you to understand your needs and automatically creates the optimal structure.

Key features:

1. Multiple file upload (PDF, Word, PPT, images, TXT, code files)
2. Web page analysis via URL
3. Integrated Internet search to enrich content
4. Referencing existing surveys as templates
5. Automatic generation of conditional branching and scoring

2. Conversational Editing Agent

The editing agent allows you to modify your survey by chatting naturally with the AI. No need to navigate through menus: simply ask for what you want, and the agent executes it.

Example commands:

• “Add a dropdown question listing Normandy departments in first position”
• “Remove question numbering”
• “Add a page break between questions 1 and 2”
• “Merge pages 2 and 3”
• “Add scoring to evaluate participants in my training”
• “Set up conditional branching based on respondent profile”

Key advantage: The agent can perform complex actions that were difficult to do manually, such as merging pages or creating dynamic lists (all departments in a region, all cities in a country, etc.).

3. Results Analysis Agent

The analysis agent transforms how you exploit your data into a dialogue. Ask your questions, follow up on answers, and get personalised insights without any statistical expertise.

What you can ask:

• “I’m in the SaaS software sector, what do you think of my NPS?”
• “What are the important elements emerging from the responses?”
• “What trends are emerging?”
• “What are the possible areas for improvement?”
• “Pull out specific data on dissatisfied users”

Technical operation: The agent runs Python code in the background to calculate averages, aggregate data and produce reliable statistics. This is not simply rephrasing, but genuine mathematical analysis.

Data pre-filtering: You can create a report with filters (e.g.: only users with NPS < 6) then dialogue with this filtered data for targeted analyses.

Comparative Table: Traditional AI vs 2025 AI Agents

The major evolution lies in complete lifecycle coverage and the ability for conversational interaction at every stage.

Criterion	Traditional AI (before Oct. 2024)	AI Agents (2025)
Coverage	Creation only	Creation + Editing + Analysis
Interaction mode	Single prompt, no dialogue	Continuous interactive conversation
Accepted sources	Text only	Files, URLs, text combined
Survey complexity	6 to 20 questions max	20 to 50+ questions, multi-page
Post-creation editing	Manual only	Conversational via agent
Results analysis	Static summary	Interactive dialogue + Python calculations
Conditional branching	Not available	Automatic and AI-modifiable

Practical Use Cases: Detailed Demonstrations

Case #1: Creating a Cybersecurity Training Quiz

Context: A trainer has a PDF course material on cybersecurity.

Process with the Creation Agent:

1. Create a new survey via the multimodal AI agent
2. Upload the training PDF file
3. Instruction: “Create a quiz to assess learner knowledge”
4. The AI analyses the document and generates the survey

Result obtained:

• Structured quiz across 2 pages with 10 relevant questions
• Automatic calculator to display the score
• Correction texts displayed for each question

Case #2: Quick Editing of an Existing Survey

Context: A satisfaction survey requires the addition of a geographical question.

Process with the Editing Agent:

1. Open the editing agent on the existing survey
2. Request: “Add a dropdown question in first position listing Normandy departments”
3. The agent thinks for a few seconds then executes

Result:

• Question added with all Normandy departments
• No need to manually create a question bank

Added value: The agent can create dynamic lists (all cities in a country, all departments in a region, etc.) without tedious manual configuration.

Case #3: Conversational Results Analysis

Context: A marketing manager wants to understand their NPS score and identify areas for improvement.

Process with the Analysis Agent:

1. Open the analysis agent on the survey
2. Question: “I’m in the SaaS software sector, what do you think of my NPS?”
3. The agent analyses the data and provides a contextualised interpretation
4. Follow-up: “What are the main reasons for dissatisfaction?”

Result:

• Genuine statistical analysis (Python calculations in the background)
• Recommendations tailored to the business sector
• Ability to dialogue and deepen the analysis

Accepted File Formats

Drag’n Survey’s AI agent accepts a very wide range of formats, with internal vectorisation management for semantic search. There is no size limit: Drag’n Survey automatically extracts relevant information.

• Images: JPEG, PNG
• Office documents: PDF, Word (.doc, .docx), PowerPoint (.ppt, .pptx)
• Spreadsheets: Excel, Google Sheets, CSV
• Text files: TXT, code files (PHP, etc.)
• Other: Mathematical calculation files and many other formats

Technical Architecture and Confidentiality

Drag’n Survey uses several AI engines depending on the features:

• OpenAI: Agentic management and coordination between agents
• Internal models: Trained by Drag’n Survey for specific tasks

Data protection: Files are uploaded and vectorised internally. Only summaries are transmitted to external APIs. Data is not part of third-party model training datasets. You remain the owner of your data.

FAQ – Frequently Asked Questions about the Drag’n Survey Multimodal AI Agent

Can an existing survey be used as a template?

Yes, the “Reference another survey” function allows you to start from an existing base (even with complex branching across 50-100 pages) and automatically adapt it to a new context by uploading new documents.

Can the agent convert a PDF survey?

Absolutely. Upload your PDF to the creation agent and request the conversion. The AI will reconstruct the survey structure with the appropriate question types.

How does AI results analysis work?

The analysis agent runs Python code in the background to calculate statistics, averages and aggregations. You can dialogue with your data, ask open questions and follow up on answers. For targeted analyses, first create a filtered report then dialogue with this data.

Does the agent handle survey distribution?

For now, the distribution part (creating links, QR codes, access settings) remains manual. The agents cover creation, editing and analysis, but not yet distribution.

What is the processing time for large documents?

For large documents, loading data into the AI may take a few minutes, particularly for the analysis agent. The time depends on survey size and response volume.

Who Should Use the Multimodal AI Agent and When?

Learn More About Surveys:
Essential satisfaction surveys, click here
Test the best online survey tools, click here
Find a powerful and simple online quiz tool, click here
Essential alternatives to Microsoft Forms software, click here
Everything about the Drag’n Survey questionnaire solution, click here

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