How to Build an AI Customer Support Agent with Paper2Agent, GLiFormer, and VC-Attention

Introduction: Why an AI agent for customer support is essential in 2026

Companies have long sought a way to provide 24/7 assistance without sacrificing quality. In 2026, the rise of models like Paper2Agent, GLiFormer and VC-Attention is turning this goal into reality. These tools enable the construction of AI agents capable of understanding documents, extracting structured data and even interpreting video interactions, making customer support faster, more accurate and personalized.

Transform support documents into intelligent agents with Paper2Agent

Paper2Agent, recently published in Nature, can convert research papers and business manuals into validated MCP (Model Context Protocol) tools. For customer support, this means being able to upload knowledge bases, FAQs and technical guides and immediately obtain an agent that can recall accurate information.

Practical example

Suppose we have a PDF with the product guide. Using Paper2Agent, we can generate an MCP tool like this:

# Generate an MCP agent for the product guide
import paper2agent as p2a

agent = p2a.create_agent(
    source='product_guide.pdf',
    description='Agent that answers questions about product features and troubleshooting',
    validation=True
)

# Use the agent in a chat system
response = agent.query('How do I replace the filter?')
print(response)

This snippet shows how to turn a single document into a reusable agent, ready to be integrated into a chat or support platform.

Extract structured data with GLiFormer for intelligent tickets

GLiFormer, a 575M-parameter encoder, excels at extracting nested JSON structures without generating intermediate tokens. In customer support, this means being able to extract fields such ascustomer,order,issueandprioritydirectly from ticket text.

Code example

# Integrate GLiFormer into a support pipeline
from knowledgator import GLiFormer

extractor = GLiFormer(model='GLiFormer-Large')

ticket_text = """
Customer: Marco Rossi
Order: #A1234
Issue: The printer will not connect to Wi-Fi.
Priority: High
"""

structured = extractor.extract(ticket_text, schema={
    'customer': 'string',
    'order_id': 'string',
    'issue': 'string',
    'priority': 'string'
})
print(structured)
# Output: {'customer': 'Marco Rossi', 'order_id': 'A1234', 'issue': 'The printer will not connect to Wi-Fi.', 'priority': 'High'}

GLiFormer ensures that each value is anchored to the original text, maintaining traceability and reducing error risks.

Accelerate video interactions using VC-Attention

Workflow

• Capture a screenshot or short video of the user's problem.
• Pass the frame through VC-Attention to extract salient visual features.
• Combine the visual information with textual context (extracted via GLiFormer) to generate a coherent response.

# Example pipeline with VC-Attention
from nunchux import VCAttention

vision_model = VCAttention(attention_type='low_bit')

frame = capture_frame(video_stream='user_screen.mp4', timestamp=5.2)
features = vision_model.extract(frame)

# Combine with extracted context
response = generate_answer(features, ticket_context=structured)
print(response)

Build the full workflow: a sample prompt

Let's put all three components together into an end-to-end support agent:

# Prompt for a unified support agent
You are a customer support agent AI. Follow these steps:
1. Use Paper2Agent to retrieve up-to-date product documentation.
2. Use GLiFormer to extract structured fields (customer, order_id, issue, priority) from the incoming ticket.
3. If the ticket includes a video, run VC-Attention on a key frame to understand visual context.
4. Synthesize a helpful answer, citing sources and attaching the extracted data.

Incoming ticket:
{{ticket_text}}

Video present: {{has_video}}

Answer:

This prompt can be directly inserted into an orchestrator like LangChain or a custom service, allowing the agent to process each request consistently.

Best practices and security considerations

• Source validation:Paper2Agent already includes validation checks; make sure to use only validated agents.
• Sensitive data handling:GLiFormer works on plain text; always apply tokenization or anonymization before extraction.
• Visual control:When using VC-Attention, store only feature vectors, not raw frames, to respect privacy.
• A/B testing:
• Run regular A/B tests to compare agent performance against human support.

Conclusions

Actionable takeaway

• Upload your knowledge base to Paper2Agent to create reusable MCP agents.
• Implement GLiFormer to automatically extract key fields from tickets.
• Integrate VC-Attention if you need to analyze screenshots or short videos.
• Test the full workflow with a representative sample of tickets.
• Monitor metrics such as resolution time, accuracy and customer satisfaction.