IT Support Triage

What you’ll build

You’ll build an intelligent IT support triage system that automatically classifies incoming tickets, attempts automated resolution for common issues using RAG-powered knowledge bases, and escalates complex problems to the appropriate specialist team with full context. This reduces resolution time for common issues from hours to seconds and ensures complex issues reach the right experts immediately. This workflow demonstrates how to:

Automatically classify and route IT support tickets
Resolve common issues using knowledge base-powered automation
Implement smart escalation based on issue complexity and severity
Provide 24/7 first-line support for employees
Build confidence-based routing for quality assurance

What the system handles

Automated resolution for common issues:

Password resets and account unlocks
VPN connection problems
Email configuration
Software installation instructions
Printer setup and troubleshooting
Network connectivity issues
Standard access requests

Smart escalation for complex issues:

Hardware failures
Security incidents
Critical system outages
Advanced software issues
Custom application problems
Database issues

Prerequisites

Before you begin, ensure you have:

MagOneAI instance with workflow builder
Knowledge base with IT runbooks (optional but highly recommended):
- Troubleshooting guides
- How-to documentation
- Common issue resolutions
- Configuration instructions
Ticketing system integration (optional) - Jira, ServiceNow, Zendesk, etc.
LLM provider configured (GPT-4, Claude 3.5 Sonnet recommended)
Notification tools - Email, Slack, MS Teams

Start with a knowledge base containing your top 20 most common IT issues and their solutions. You can expand over time as you identify more patterns.

Architecture

The IT support workflow uses classification-based routing with automated resolution attempts:

Trigger (Ticket/Chat submission)
    ↓
Triage Agent (Classify: category, severity, complexity)
    ↓
Condition Node: Known issue with solution?
    ├── YES → Resolution Agent (with RAG) → Auto-reply to user
    └── NO → Condition Node: Severity level?
            ├── CRITICAL → Escalate to L2 + Alert on-call
            └── NORMAL → Create ticket with context + Notify L1 queue

Why this architecture works

Fast Resolution

Common issues resolved in under 30 seconds vs. 2-8 hour wait for human support

Right Routing

Complex issues reach specialists immediately with full context, not bounced between teams

24/7 Coverage

First-line support available anytime, reducing pressure on on-call staff

Learning System

Knowledge base grows over time, handling more issues automatically

Step-by-step build

Create IT knowledge base with runbooks

Build a knowledge base of IT solutions and procedures.Gather documentation:

Common Issue Resolutions
- Password reset procedures
- VPN troubleshooting steps
- Email setup guides (Outlook, Gmail, mobile)
- Wi-Fi connection issues
- Printer setup and common errors
- Software installation guides
- Browser troubleshooting
Access Request Procedures
- How to request software licenses
- Access control procedures
- VPN access requests
- Admin rights request process
- Shared drive access
Security Procedures
- Phishing reporting
- Lost device protocol
- Security incident reporting
- Data breach response
- Suspicious activity reporting
Hardware Guides
- Laptop setup guides
- Monitor and peripheral setup
- Mobile device configuration
- Hardware replacement process
- Warranty and repair procedures

Create the knowledge base:

Navigate to Knowledge Bases → Create New
Name: “IT Support Runbooks”
Upload documents:
- IT_Runbook_Common_Issues.pdf
- VPN_Troubleshooting_Guide.pdf
- Email_Configuration_Guide.pdf
- Network_Troubleshooting.pdf
- Access_Request_Procedures.pdf
- Security_Incident_Procedures.pdf
Configure chunking: Automatic (procedure-based chunking works well)
Wait for processing to complete

Creating effective runbook documents

Runbook structure for RAG:

# Issue: Cannot Connect to VPN

## Symptoms
- VPN client shows "Connection failed"
- Error message: "Authentication failed"
- Unable to access internal resources

## Common Causes
1. Expired password
2. VPN client out of date
3. Network blocking VPN ports
4. VPN account not activated

## Resolution Steps

### Step 1: Verify Credentials
1. Ensure you're using your network username (not email)
2. Check if password has expired
3. Try logging into the employee portal to verify password

### Step 2: Update VPN Client
1. Open VPN client
2. Go to Help → Check for Updates
3. If update available, install and restart

### Step 3: Check Network Connection
1. Ensure you're connected to internet
2. Try accessing a website (e.g., google.com)
3. If on corporate guest Wi-Fi, VPN may be blocked

### Step 4: Verify VPN Account Status
1. New employees: VPN activated 24h after start date
2. Check with IT if account is active
3. Contractors: Ensure VPN access was requested

## When to Escalate
- None of the above steps work
- Error message is different from above
- VPN was working before and suddenly stopped

## Related Issues
- See also: Network Connectivity Issues
- See also: Password Reset Procedures

This structure helps RAG retrieve the right information at the right time.

Create the triage agent

Build an agent that classifies incoming support requests.Agent configuration:Name: IT Support Triage Agent Model: GPT-4 or Claude 3.5 SonnetPersona:

You are an IT support triage specialist who classifies incoming IT support requests.

Analyze the support request and classify it across three dimensions:

1. CATEGORY (primary issue type):
   - NETWORK: Wi-Fi, VPN, connectivity issues
   - ACCESS: Passwords, permissions, account access
   - SOFTWARE: Application issues, installations, licenses
   - HARDWARE: Laptop, monitor, peripherals, phone
   - EMAIL: Email configuration, delivery, mailbox issues
   - SECURITY: Phishing, malware, suspicious activity, lost device
   - PRINTING: Printer setup, connection, print queue
   - OTHER: Anything not clearly fitting above categories

2. SEVERITY:
   - CRITICAL: System down, security incident, business-blocking issue
   - HIGH: Major functionality impaired, affecting multiple users
   - MEDIUM: Issue affecting single user, workaround available
   - LOW: Minor issue, feature request, question

3. RESOLVABILITY (can this be auto-resolved?):
   - AUTO: Common issue with documented solution (e.g., VPN setup, password reset)
   - ESCALATE: Requires human expertise or special access
   - UNCERTAIN: Need more information to determine

4. CONFIDENCE (how confident are you in this classification?):
   - HIGH: Clear, well-described issue matching known patterns
   - MEDIUM: Issue description is somewhat vague but likely classification
   - LOW: Unclear description, ambiguous symptoms

Output structured JSON:
{
  "category": "NETWORK",
  "severity": "MEDIUM",
  "resolvability": "AUTO",
  "confidence": "HIGH",
  "summary": "User unable to connect to VPN - authentication failed error",
  "extracted_info": {
    "error_message": "Authentication failed",
    "affected_system": "VPN",
    "user_platform": "Windows laptop"
  },
  "reasoning": "Clear VPN connection issue with specific error message. This is a common issue with documented troubleshooting steps."
}

Important:
- ALWAYS classify SECURITY issues as CRITICAL severity
- Be conservative with AUTO resolvability - when in doubt, choose ESCALATE
- If you need more information, mark as UNCERTAIN and suggest questions to ask

Configuration:

Temperature: 0.2 (consistent classification)
Structured output: Enabled (JSON schema)
Timeout: 15 seconds

Create the resolution agent

Build an agent that attempts to resolve issues using the knowledge base.Agent configuration:Name: IT Issue Resolution Agent Model: GPT-4 or Claude 3.5 Sonnet Knowledge Base: IT Support Runbooks (RAG enabled)Persona:

You are an IT support specialist who helps users resolve technical issues using documented procedures.

Your task:
1. Search the IT knowledge base for solutions to the reported issue
2. Provide clear, step-by-step instructions
3. Include troubleshooting steps in logical order
4. Explain WHY each step helps (builds user understanding)
5. Specify when to escalate if steps don't work
6. Be encouraging and patient

Response format:

Hi [User Name],

I can help you with this issue. Here's what to try:

[Step-by-step instructions from knowledge base]

**What to expect:**
[What should happen if steps work]

**If this doesn't work:**
[When to escalate and how]

**Why this happens:**
[Brief explanation of the issue - helps prevent recurrence]

Let me know if this resolves the issue or if you need further assistance!

Best regards,
IT Support

Important guidelines:
- Only provide solutions from the knowledge base (no hallucination)
- If no solution in KB, say so and escalate
- Include screenshots or links if referenced in KB
- Adapt technical language to user's expertise level
- Always cite the source document
- If multiple solutions exist, provide the most common/simple one first

Configuration:

Temperature: 0.3 (balanced creativity and consistency)
RAG settings:
- Retrieve: 5 chunks
- Relevance threshold: 0.75
- Enable reranking
Max tokens: 1000
Timeout: 30 seconds

Build the workflow with conditional routing

Construct the triage and routing workflow.Node 1: Trigger

Type: Manual Trigger or API Trigger
Inputs:
- user_name (text)
- user_email (text)
- issue_description (text, long)
- ticket_id (text, optional - if integrated with ticketing system)

Node 2: Triage Agent

Agent: IT Support Triage Agent
Input: {{trigger.issue_description}}
Output variable: triage_result

Node 3: Condition - Known Issue?

Condition:

{{triage_result.resolvability}} == "AUTO"
AND
{{triage_result.confidence}} == "HIGH"

True branch: Attempt auto-resolution
False branch: Go to severity check

Node 4 (True branch): Resolution Agent

Agent: IT Issue Resolution Agent

Input:

User Name: {{trigger.user_name}}
Issue: {{trigger.issue_description}}
Category: {{triage_result.category}}
Summary: {{triage_result.summary}}

Enable RAG: Yes
Output variable: resolution

Node 5 (True branch): Send Auto-Reply

Tool: Email or Chat response
To: {{trigger.user_email}}
Subject: Re: Your IT Support Request - Possible Solution

Body:

{{resolution}}

---
Ticket ID: {{trigger.ticket_id}}
This is an automated response. If the issue persists, reply to this email and it will be escalated to our support team.

Node 6 (False branch): Condition - Severity Check

Condition:

{{triage_result.severity}} == "CRITICAL"

True branch: Critical escalation
False branch: Normal ticket creation

Node 7 (Critical branch): Alert On-Call Team

Tool: Notification (Slack, PagerDuty, Email)

Message:

🚨 CRITICAL IT ISSUE

Ticket: {{trigger.ticket_id}}
User: {{trigger.user_name}}
Category: {{triage_result.category}}
Issue: {{triage_result.summary}}

Full Description:
{{trigger.issue_description}}

Action Required: Immediate response needed

Node 8 (Normal branch): Create Ticket with Context

Tool: Ticketing system API or Email to IT queue

Create ticket with:

Title: [{{triage_result.category}}] {{triage_result.summary}}
Description: {{trigger.issue_description}}
Reporter: {{trigger.user_name}} ({{trigger.user_email}})
Priority: {{triage_result.severity}}
Labels: {{triage_result.category}}, auto-triaged

AI Triage Analysis:
- Category: {{triage_result.category}}
- Severity: {{triage_result.severity}}
- Confidence: {{triage_result.confidence}}
- Reasoning: {{triage_result.reasoning}}

Node 9 (Both escalation branches): Notify User

Tool: Email
Subject: Your IT Support Request Has Been Received

Body:

Hi {{trigger.user_name}},

Thank you for contacting IT support.

Your issue has been classified as:
- Category: {{triage_result.category}}
- Priority: {{triage_result.severity}}
- Ticket ID: {{trigger.ticket_id}}

{{#if triage_result.severity == "CRITICAL"}}
This is a critical issue and our on-call team has been alerted. You should receive a response within 30 minutes.
{{else}}
Your ticket has been assigned to our support team. Expected response time: 4-8 business hours.
{{/if}}

We'll update you as soon as we have more information.

Best regards,
IT Support Team

This multi-branch workflow ensures every request is handled appropriately: auto-resolved, escalated urgently, or queued normally with full context.

Add confidence-based quality assurance

Ensure auto-resolved issues were actually resolved.Enhancement: Follow-up confirmationAfter auto-resolution (Node 5), add a Human Task node that asks the requester to confirm the issue was actually resolved. MagOneAI has no standalone timer/delay node, so the wait is handled by the Human Task’s own timeout.Node 5a: Human Task - Confirm Resolution

Task type: Approval
Assignee: The requester’s user ID
Title: Was your IT issue resolved?

Description:

Earlier we provided automated assistance for your IT issue:
"{{triage_result.summary}}"

Was this resolved successfully?

Decision question: A choice question with options “Yes, resolved” and “No, still having issues”
Timeout: Set timeout_minutes (e.g. 1440 for 24 hours) so the task auto-resolves if the requester doesn’t respond
On “No” or timeout: Route to ticket creation (escalate) via an approval branch
On “Yes”: Close ticket, mark as successfully auto-resolved

This creates a quality assurance loop and catches cases where the auto-resolution didn’t work.

Test with common IT scenarios

Validate the workflow with realistic support requests.Test Case 1: Common issue (VPN)

Input:

User: John Smith
Email: john@company.com
Issue: "I can't connect to the VPN. It says 'Authentication failed'. I'm working from home on my company laptop."

Expected:
- Triage: NETWORK, MEDIUM severity, AUTO resolvable, HIGH confidence
- Resolution: VPN troubleshooting steps from knowledge base
- Output: Auto-reply with step-by-step instructions
Verify:
- ✅ Correct classification
- ✅ Relevant KB article retrieved
- ✅ Clear instructions provided
- ✅ Follow-up scheduled

Test Case 2: Critical security issue

Input:

User: Sarah Johnson
Email: sarah@company.com
Issue: "I clicked on a link in an email and now my computer is acting strange. Lots of pop-ups and it's very slow. I think it might be malware."

Expected:
- Triage: SECURITY, CRITICAL severity, ESCALATE, HIGH confidence
- Route: Immediate alert to on-call
- Output: Critical alert sent + user notified of urgent handling
Verify:
- ✅ Correctly identified as security incident
- ✅ Marked as CRITICAL
- ✅ On-call team alerted immediately
- ✅ User notified of urgent response

Test Case 3: Hardware issue (escalation)

Input:

User: Mike Chen
Email: mike@company.com
Issue: "My laptop won't turn on. I've tried charging it overnight but still nothing. The power light doesn't come on at all."

Expected:
- Triage: HARDWARE, HIGH severity, ESCALATE, HIGH confidence
- Route: Create ticket with context
- Output: Ticket created + user notified
Verify:
- ✅ Correctly classified as hardware
- ✅ Marked for escalation (can’t auto-resolve hardware)
- ✅ Ticket includes full triage analysis
- ✅ User notified with expected timeline

Test Case 4: Vague description (low confidence)

Input:

User: Lisa Park
Email: lisa@company.com
Issue: "My computer is slow"

Expected:
- Triage: OTHER/SOFTWARE, MEDIUM severity, UNCERTAIN, LOW confidence
- Route: Create ticket (don’t attempt auto-resolution due to low confidence)
- Output: Ticket created asking for more information
Verify:
- ✅ Doesn’t attempt auto-resolution (low confidence)
- ✅ Ticket includes request for more details
- ✅ User asked to provide additional context

Test Case 5: Password reset

Input:

User: David Lee
Email: david@company.com
Issue: "I forgot my password and I'm locked out. How do I reset it?"

Expected:
- Triage: ACCESS, MEDIUM severity, AUTO, HIGH confidence
- Resolution: Password reset instructions from KB
- Output: Auto-reply with self-service reset link and instructions
Verify:
- ✅ Auto-resolved with password reset procedure
- ✅ Includes link to self-service portal
- ✅ Clear instructions provided

Monitor false positives closely: cases marked AUTO but couldn’t actually be auto-resolved. Adjust confidence thresholds if you see too many failed auto-resolutions.

Integrate with ticketing system (optional)

Connect to your existing ticketing system for seamless operations.Supported integrations:

Jira Service Management
ServiceNow
Zendesk
Freshdesk
Custom ticketing systems (via API)

Integration approach:

Add API Tool for your ticketing system
- Configure authentication (API key, OAuth)
- Map fields (title, description, priority, category)
Modify workflow nodes:
- Node 8 (Create Ticket): Use ticketing API instead of email
- Trigger: Accept tickets from ticketing system webhook
- Auto-resolution: Update ticket status to “Resolved - Auto”
- Escalation: Update ticket and assign to appropriate team
Bidirectional sync:
- Ticket updates from system → MagOneAI
- MagOneAI resolution → Update ticket
- Comments and attachments synced

Benefits:

Centralized ticket management
No duplicate systems
Full audit trail
Reporting and analytics
SLA tracking

Key concepts demonstrated

Conditional Routing

Dynamically route requests based on AI classification, severity, and confidence scores

RAG with Technical Docs

Use knowledge bases of runbooks and procedures to power automated resolutions

Multi-Branch Decision Trees

Build complex routing logic: auto-resolve vs. escalate vs. critical alert

Tool Integration

Connect to ticketing systems, notification tools, and communication platforms

Confidence-Based Actions

Make routing decisions based on AI confidence to balance automation and quality

Quality Assurance Loops

Follow up on auto-resolutions to ensure they actually worked

Customization ideas

Extend this IT support workflow to handle more scenarios:

Add VPN diagnostic sub-workflow

Create a specialized workflow for VPN troubleshooting:VPN Diagnostic Agent:

Asks clarifying questions: OS? Error message? Home or office network?
Runs through decision tree of common VPN issues
Provides specific solutions based on answers
Escalates if all diagnostics fail

Implementation:

When triage identifies VPN issue, route to VPN diagnostic workflow
Multi-turn conversation to gather diagnostic info
Step-by-step troubleshooting with user feedback
Final resolution or escalation with full diagnostic context

Benefits:

Higher auto-resolution rate for VPN (common issue)
Better context when escalating
Educates users on self-service diagnostics

Integrate with monitoring tools

Proactive issue detection and resolution:Monitoring integration:

Connect to Datadog, New Relic, Nagios, etc.
Detect patterns: “Multiple VPN failures from specific location”
Trigger proactive workflows

Proactive support:

Alert: “VPN server degraded performance”
Action: Pre-emptively notify affected users
Message: “We’re aware of VPN issues and working on it”
Reduce incoming tickets by communicating known issues

Auto-remediation:

Detect: “Printer offline”
Action: Restart print spooler service
Verify: Check if printer is back online
Notify: “Printer issue resolved automatically”

Benefits:

Shift from reactive to proactive support
Reduce ticket volume
Improve user experience

Add feedback loop for accuracy

Continuously improve triage and resolution:Feedback collection:

After resolution: “Did this solve your problem? Yes/No”
After escalation: Support agent marks if triage was correct
Track: Which categories/issues have low auto-resolution success

Analytics:

Auto-resolution success rate by category
Triage accuracy (were classifications correct?)
Knowledge base gap analysis (common issues without solutions)
Average time to resolution (auto vs. human)

Improvement cycle:

Weekly review of misclassified tickets
Add missing solutions to knowledge base
Refine triage agent persona
Update category definitions
A/B test triage prompt changes

Implementation:

Add feedback collection nodes
Store feedback with ticket data
Build analytics dashboard
Schedule weekly review process

Connect to asset management

Device-specific troubleshooting:Asset management integration:

Connect to asset management system (e.g., Jamf, InTune, custom DB)
Query user’s device info: model, OS, installed software, warranty

Enhanced triage:

“Laptop won’t turn on” + Asset lookup → Check warranty status
“Software won’t install” + Asset lookup → Check if compatible with OS version
“Slow computer” + Asset lookup → Check device age and specs

Automated actions:

Warranty valid → Create RMA ticket
Warranty expired → Offer replacement options
Incompatible software → Suggest alternatives
Old device → Flag for upgrade consideration

Benefits:

More accurate diagnostics
Faster resolution paths
Better inventory management
Proactive hardware refresh planning

Build self-service portal

Empower users to self-diagnose:Self-service features:

Interactive troubleshooters: Wizard-style Q&A for common issues
Knowledge base search: User can search runbooks directly
Status dashboard: Show known issues and outages
Common requests: One-click password reset, VPN setup guide

Chat interface:

IT Support chatbot available on portal
Natural language: “How do I connect to Wi-Fi?”
Escalation: “Contact human support” button always visible

Workflow enhancement:

Portal interactions feed into triage workflow
Track: Which self-service articles are most used
Update: Keep popular articles current and clear
Measure: Reduction in ticket volume from self-service

Implementation:

Build web portal with chat widget
Connect to same MagOneAI agents and knowledge base
Add analytics tracking
Promote portal during onboarding

Add multi-language support

Support global teams:Language detection:

Detect user’s language from issue description
Or detect from user profile in HRIS

Translation approach:

Translate incoming issue to English for triage
Process with English knowledge base
Translate resolution back to user’s language

Multilingual knowledge base:

Maintain runbooks in multiple languages
Route to language-specific KB based on user language
Ensure terminology consistency across languages

Supported languages:

Based on your employee demographics
Common: English, Spanish, French, German, Mandarin, Japanese

Implementation:

Add translation agents or API (Google Translate, DeepL)
Configure multilingual embedding models
Test thoroughly for accuracy
Have native speakers review critical translations

Create escalation SLA automation

Ensure timely responses:SLA tracking:

Define SLAs by severity:
- CRITICAL: 30 minutes
- HIGH: 4 hours
- MEDIUM: 8 hours
- LOW: 24 hours

Automated reminders:

50% of SLA: Reminder to assigned agent
75% of SLA: Escalate to team lead
90% of SLA: Escalate to manager + alert
SLA breach: Create incident, notify leadership

Smart escalation:

If agent hasn’t responded, auto-reassign to available agent
If team is overloaded, offer self-service resources to user
If repeated escalations, suggest knowledge base improvement

Implementation:

Add timer nodes to track elapsed time
Condition nodes at SLA checkpoints
Notification nodes for reminders and escalations
Integrate with ticketing system SLA features

Reporting:

SLA compliance rate
Breaches by category and team
Average resolution time
Identify bottlenecks and training needs

Example triage results

Here’s what triage classifications look like:

Auto-Resolvable
Critical Escalation
Normal Escalation
Low Confidence

Input: “I can’t connect to Wi-Fi at the office. It keeps asking for a password but won’t accept mine.”Triage Output:

{
  "category": "NETWORK",
  "severity": "MEDIUM",
  "resolvability": "AUTO",
  "confidence": "HIGH",
  "summary": "Unable to connect to office Wi-Fi - password rejection",
  "extracted_info": {
    "network_type": "Office Wi-Fi",
    "symptom": "Password not accepted",
    "location": "Office"
  },
  "reasoning": "Common Wi-Fi issue. We have documented troubleshooting for corporate Wi-Fi authentication failures."
}

Action: Auto-resolve with Wi-Fi setup guide from knowledge base

Input: “URGENT: Our production server is down. Customers can’t access the application. This started 10 minutes ago.”Triage Output:

{
  "category": "HARDWARE",
  "severity": "CRITICAL",
  "resolvability": "ESCALATE",
  "confidence": "HIGH",
  "summary": "Production server outage - customer-facing impact",
  "extracted_info": {
    "affected_system": "Production server",
    "impact": "Customer-facing application down",
    "duration": "10 minutes",
    "business_impact": "Revenue affecting"
  },
  "reasoning": "Production outage with customer impact requires immediate expert intervention. This is a critical incident."
}

Action: Alert on-call engineer immediately + Create P1 incident

Input: “My Excel is crashing every time I try to open a specific file. Other files work fine. I need this file for a presentation tomorrow.”Triage Output:

{
  "category": "SOFTWARE",
  "severity": "HIGH",
  "resolvability": "ESCALATE",
  "confidence": "MEDIUM",
  "reasoning": "File-specific Excel crash suggests corruption or compatibility issue. Requires diagnostic beyond standard troubleshooting. High severity due to time sensitivity (presentation tomorrow)."
}

Action: Create ticket assigned to L2 support + User notified of 4-hour SLA

Input: “Things aren’t working right.”Triage Output:

{
  "category": "OTHER",
  "severity": "MEDIUM",
  "resolvability": "UNCERTAIN",
  "confidence": "LOW",
  "summary": "Vague issue description - insufficient details for classification",
  "extracted_info": {
    "symptoms": "Unspecified"
  },
  "reasoning": "Issue description too vague to classify accurately. Need more information about what 'things' aren't working and what 'not working right' means."
}

Action: Create ticket with request for more details: “Could you please provide more information? What specifically isn’t working? What were you trying to do? Any error messages?”

Measuring success

Track these metrics to demonstrate value: Efficiency metrics:

% of tickets auto-resolved (target: 30-40% for mature systems)
Average time to first response (auto vs. human)
Average time to resolution (auto vs. human)
% of tickets correctly triaged
Reduction in L1 ticket volume

Quality metrics:

Auto-resolution success rate (user confirms resolved)
Triage classification accuracy
Escalation appropriateness (were escalations necessary?)
SLA compliance rate
User satisfaction scores

Business impact:

IT team hours saved per month
Reduction in average resolution time
24/7 coverage without additional headcount
Improved employee productivity (faster resolutions)
Knowledge base ROI (solutions reused via RAG)

Example success metrics after 3 months:

- 850 tickets processed
- 38% auto-resolved (323 tickets)
- Average auto-resolution time: 45 seconds
- Average human resolution time: 6.2 hours
- Estimated time saved: 2,000 hours (323 tickets × 6.2 hours)
- User satisfaction: 4.1/5 for auto-resolutions
- Triage accuracy: 89%
- SLA compliance: 94% (up from 78% before automation)

Next steps

Now that you’ve built an IT support triage workflow, explore related cookbooks:

HR Policy Assistant - Similar RAG-based support automation for HR
Document Compliance Review - Another conditional routing workflow
Sales Intelligence Assistant - See more chat-triggered automation

Need help integrating with your specific ticketing system or building custom diagnostic workflows? Contact our solutions team for IT automation guidance.

Getting Started

Platform Guide

Agents

Workflow Builder

Tools & Integrations

Models & Providers

Cookbooks

Security & Administration

IT Support Triage

What you’ll build

What the system handles

Prerequisites

Architecture

Why this architecture works

Fast Resolution

Right Routing

24/7 Coverage

Learning System

Step-by-step build

Key concepts demonstrated

Conditional Routing

RAG with Technical Docs

Multi-Branch Decision Trees

Tool Integration

Confidence-Based Actions

Quality Assurance Loops

Customization ideas

Example triage results

Measuring success

Next steps

​What you’ll build

​What the system handles

​Prerequisites

​Architecture

​Why this architecture works

Fast Resolution

Right Routing

24/7 Coverage

Learning System

​Step-by-step build

​Key concepts demonstrated

Conditional Routing

RAG with Technical Docs

Multi-Branch Decision Trees

Tool Integration

Confidence-Based Actions

Quality Assurance Loops

​Customization ideas

​Example triage results

​Measuring success

​Next steps

What you’ll build

What the system handles

Prerequisites

Architecture

Why this architecture works

Step-by-step build

Key concepts demonstrated

Customization ideas

Example triage results

Measuring success

Next steps