How to Build an AI Avatar IT Support Agent That Can See Your Screen (LiveKit + Beyond Presence)
IT teams waste 30% of their time on password resets and basic account issues. This step-by-step guide shows how to build an AI avatar agent that handles tier-1 support tickets autonomously - complete with screen sharing, account unblocking, and email ticket creation. No more repetitive help desk calls.
The IT Support Problem AI Can Solve
Every IT manager knows the frustration: 40% of help desk tickets are for password resets and account access issues. Employees wait in queues while your team performs the same repetitive troubleshooting steps. The solution? An AI avatar agent that handles tier-1 support autonomously.
Traditional chatbots fail because they can't see screens or perform actions. This guide shows how to build an AI agent that combines natural conversation with visual understanding and administrative capabilities. At 3:15 in the video, you'll see the agent successfully guide a user through a login issue by viewing their screen and spotting a username format error.
Key stat: Companies using AI support agents report 65% faster resolution times for common IT issues and 30% reduction in help desk staffing costs.
LiveKit Setup for Real-Time Voice & Video
LiveKit provides the real-time communication backbone for our AI agent. Unlike traditional WebRTC solutions, LiveKit offers built-in features perfect for AI applications - including server-side processing hooks and flexible room management.
The setup process (shown at 5:42 in the video) involves:
- Creating a free LiveKit account and project
- Installing the LiveKit CLI and Python SDK
- Configuring environment variables for authentication
- Setting up a virtual environment with uv
One critical step often missed is configuring the video_enabled=True parameter (shown at 18:30), which allows the agent to receive screen shares from users. Without this, the agent would be limited to audio-only interactions.
Building the Core Agent Logic in Python
The agent's intelligence comes from combining OpenAI's language model with custom Python logic. At 9:15 in the tutorial, you'll see the basic agent structure that handles conversation flow:
from livekit import agents from openai import OpenAI class SupportAgent: def __init__(self): self.llm = OpenAI() self.tools = [UnblockUserTool(), SendEmailTool()] async def handle_request(self, request): # Process user input # Decide tool usage # Generate responses The key innovation is the agent's ability to switch between free-form conversation and structured tool usage. When it detects specific scenarios (like a blocked account message on screen), it seamlessly transitions to executing the appropriate function.
Implementing Screen Sharing Capabilities
Screen sharing transforms the agent from a basic chatbot to a true virtual support technician. The implementation involves:
- Configuring LiveKit's video capture settings
- Processing frames with OpenCV for text/UI element detection
- Creating conditional logic based on screen content
At 22:10 in the video, you'll see the agent identify a username format error ("The username should start with a backslash...") by analyzing the shared screen. This visual understanding is what makes the solution so effective for IT support scenarios.
Pro Tip: Add typing sound effects (implemented at 41:50) when the agent processes visual information. This provides natural feedback that the agent is "thinking" about what it sees.
Creating Function Tools for Account Management
The real power comes from function tools that let the agent perform actions. The tutorial shows two critical examples:
- Unblock User Tool: Automatically removes account blocks by modifying a simulated corporate system (25:30)
- Send Email Tool: Creates ticket summaries and sends them to users (38:15)
Each tool follows the same pattern:
@agents.tool async def unblock_user(run_ctx, username: str): """Unblocks users so they can login again""" # Implementation logic return "User unblocked successfully" The magic happens when the agent automatically selects the right tool based on the conversation and screen content, as demonstrated at 30:45 when it unblocks an account after seeing the "account blocked" message.
Adding the AI Avatar with Beyond Presence
Beyond Presence brings the agent to life with a realistic avatar that:
- Lip-syncs with generated speech
- Displays natural facial expressions
- Provides visual presence during screen sharing
The integration process (shown at 34:20) involves:
- Creating a Beyond Presence account
- Selecting an avatar from their library
- Adding API keys to your environment variables
- Updating the agent code to use the avatar
The result (visible at 36:05) is a professional-looking support agent that builds user trust far better than a voice-only interface.
Automating Email Ticket Creation
No support interaction is complete without proper documentation. The email tool automatically:
- Requests the user's email address (39:30)
- Generates a summary of the solution
- Sends it via Gmail's SMTP server
- Provides visual confirmation (42:20)
The implementation uses Python's smtplib with proper security measures:
def send_email(to, subject, message): msg = MIMEText(message) msg['Subject'] = subject msg['From'] = EMAIL msg['To'] = to with smtplib.SMTP_SSL('smtp.gmail.com', 465) as smtp: smtp.login(EMAIL, PASSWORD) smtp.send_message(msg) This creates a complete audit trail while saving agents hours of manual documentation.
Building a Custom Frontend Interface
The final piece is a professional interface for users to interact with the agent. The tutorial provides a React starter template (45:50) that includes:
- Video call interface with screen sharing
- Real-time chat transcript
- Visual notifications for tool executions
- Branding customization options
The most innovative feature is the RPC handler (48:30) that shows visual confirmations when the agent performs actions like unblocking accounts. This creates transparency about what the agent is doing behind the scenes.
Implementation Tip: Use LiveKit's room RPC methods to trigger frontend updates from your Python tools, creating seamless visual feedback.
Watch the Full Tutorial
See the complete implementation from start to finish in the video tutorial. At 15:30, you'll see the critical moment when the agent first views a user's screen and identifies a login format error. At 30:45, watch it automatically unblock an account after detecting the "blocked" message.
Key Takeaways
This implementation demonstrates how AI can transform IT support by combining conversational abilities with visual understanding and administrative capabilities. The complete solution handles the 40% of tickets that are repetitive while freeing human agents for complex issues.
In summary: 1) LiveKit enables real-time voice/video, 2) Function tools give the agent administrative capabilities, 3) Beyond Presence adds professional avatar presence, and 4) A custom frontend creates a polished user experience.
Frequently Asked Questions
Common questions about AI avatar IT support agents
You'll need four core components: LiveKit for real-time voice/video communication, Beyond Presence for the AI avatar visualization, Python for the backend logic, and a React frontend for the user interface.
The agent combines multiple technologies: speech recognition for understanding users, natural language processing for conversation, computer vision for screen analysis, and custom function tools for performing actions like unblocking accounts.
- LiveKit handles the real-time communication layer
- Beyond Presence provides the animated avatar
- Python implements the agent's decision logic
- React creates the user interface
The agent uses LiveKit's video capabilities to view shared screens. When a user shares their screen, the agent receives the video stream and can analyze it using OpenCV for text recognition and UI element detection.
By setting video_enabled=True in the session configuration, the agent gains visual understanding capabilities. This allows it to guide users through issues by seeing exactly what they see, just like a human support technician would.
- Requires user permission to share screen
- Uses OpenCV for text/UI element detection
- Analyzes screen content in real-time
- Provides specific guidance based on what it sees
Yes, through function tools. The tutorial demonstrates creating an unblock_user tool that modifies a simulated corporate system. When the agent detects a "account blocked" message on screen, it automatically executes this tool to resolve the issue.
In real implementations, you would connect this to your actual user management system (like Active Directory) with proper authentication. The Python function makes API calls or database modifications to perform the administrative action.
- Yes - through authenticated function tools
- Connects to your existing systems via API
- Performs actions only after visual confirmation
- Creates audit logs of all actions taken
The agent uses a send_email function tool connected to Gmail's SMTP server. After resolving an issue, it requests the user's email address and sends a formatted summary of the solution, including any steps taken.
The implementation uses Python's smtplib with proper security measures. For production use, we recommend:
- Using app-specific passwords (not main account credentials)
- Implementing rate limiting to prevent abuse
- Storing email templates separately from code
- Adding DKIM/SPF records for deliverability
Beyond Presence provides pre-built, realistic avatars with natural animations that sync perfectly with speech. Their solution offers several advantages over building avatars from scratch:
The platform handles all the complex animation synchronization, lip-sync, and expression generation. This lets you focus on the agent's functionality rather than visual presentation. Their avatars also integrate seamlessly with LiveKit's architecture.
- 30+ pre-built professional avatars
- Automatic lip-sync to generated speech
- Natural blinking and facial expressions
- Enterprise-grade reliability and scalability
The tutorial provides a React starter template that's designed for easy customization. Even developers with basic React knowledge can modify colors, layouts, and branding elements. More advanced customizations require additional JavaScript skills.
Key customization points include:
- CSS variables for color schemes
- Config files for branding elements
- Component structure for layout changes
- Notification templates for action feedback
Basic Python knowledge is needed for the agent logic and JavaScript/React for the frontend. The tutorial breaks down each component clearly, and complete code is provided. No advanced AI expertise is required as it uses existing APIs.
For teams without these skills, GrowwStacks offers:
- Pre-built agent templates
- Custom development services
- Managed deployment options
- Ongoing maintenance plans
GrowwStacks specializes in building custom AI automation solutions like this IT support agent. We handle the complete implementation from design to deployment, including integration with your existing systems.
Our service includes:
- Custom workflow design tailored to your support processes
- Integration with your existing IT systems (Active Directory, ticketing, etc.)
- Branded avatar creation matching your company identity
- Ongoing maintenance and updates
Ready to Automate Your IT Support?
Every minute your team spends on password resets is a minute lost from strategic projects. GrowwStacks can have your AI support agent live in 2 weeks - handling 40% of tickets automatically from day one.