Customer Wiki Agent

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Overview

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What the Agent Needs to Accomplish

• Analyze support tickets and customer interactions to identify common questions
• Extract key information from product documentation and manuals
• Generate and update knowledge base articles
• Answer FAQs using existing knowledge base
• Identify knowledge gaps and suggest new articles
• Track article effectiveness and update recommendations

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How Pylar Helps

• Unified Data Access: Combining support tickets, product docs, and knowledge base in one view
• Pattern Recognition: Analyzing ticket patterns to identify common issues
• Content Analysis: Querying product documentation and existing articles
• Real-time Updates: Keeping knowledge base current with latest interactions
• Governed Access: Ensuring only appropriate content is accessible

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Without Pylar vs With Pylar

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Without Pylar

• ❌ Multiple systems to query (Zendesk, Confluence, Notion, product docs)
• ❌ Complex API integrations for each system
• ❌ Manual analysis of ticket patterns
• ❌ Difficult to correlate support data with product documentation
• ❌ No unified view of knowledge gaps
• ❌ Time-consuming content updates
• ❌ Limited ability to track article effectiveness

• 3-4 different API integrations
• Custom analytics for ticket patterns
• Manual content curation
• Complex data correlation logic
• ~3-4 weeks development time

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With Pylar

• ✅ Single endpoint for all knowledge sources
• ✅ SQL views combine tickets, docs, and articles
• ✅ Easy pattern analysis through SQL queries
• ✅ Real-time knowledge base updates
• ✅ Built-in analytics on article effectiveness
• ✅ Simple content updates via view modifications

• Connect 3-4 data sources (1 hour)
• Create knowledge base views (2 hours)
• Build MCP tools with AI (1 hour)
• Connect to agent builder (15 minutes)
• Total: ~4-5 hours

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Step-by-Step Implementation

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Step 1: Connect Data Sources

1. Connect Zendesk (Support tickets, articles)
2. Connect Confluence/Notion (Product documentation)
3. Connect Product Analytics (Feature usage, user behavior)
4. Connect Knowledge Base (Existing articles, FAQs)

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Step 2: Create Knowledge Base Views

CREATE VIEW support_ticket_patterns AS
SELECT 
  t.category,
  t.subject,
  COUNT(*) as frequency,
  AVG(t.resolution_time_hours) as avg_resolution_time,
  GROUP_CONCAT(DISTINCT t.tags) as common_tags,
  MAX(t.created_date) as last_occurrence
FROM zendesk.tickets t
WHERE t.status = 'resolved'
GROUP BY t.category, t.subject
HAVING COUNT(*) >= 5
ORDER BY frequency DESC;

CREATE VIEW knowledge_gaps AS
SELECT 
  t.subject,
  t.category,
  COUNT(*) as ticket_count,
  CASE 
    WHEN kb.article_id IS NULL THEN 'No Article'
    ELSE 'Article Exists'
  END as article_status,
  AVG(t.resolution_time_hours) as avg_resolution_time
FROM zendesk.tickets t
LEFT JOIN knowledge_base.articles kb 
  ON LOWER(t.subject) LIKE LOWER(CONCAT('%', kb.title, '%'))
WHERE t.created_date >= DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY)
GROUP BY t.subject, t.category, kb.article_id
HAVING COUNT(*) >= 3 AND kb.article_id IS NULL
ORDER BY ticket_count DESC;

CREATE VIEW article_effectiveness AS
SELECT 
  kb.article_id,
  kb.title,
  kb.category,
  kb.view_count,
  COUNT(DISTINCT t.ticket_id) as related_tickets,
  AVG(t.resolution_time_hours) as avg_resolution_time,
  CASE 
    WHEN kb.view_count > 0 
    THEN (COUNT(DISTINCT t.ticket_id) / kb.view_count) * 100
    ELSE 0
  END as ticket_to_view_ratio
FROM knowledge_base.articles kb
LEFT JOIN zendesk.tickets t 
  ON LOWER(t.subject) LIKE LOWER(CONCAT('%', kb.title, '%'))
GROUP BY kb.article_id, kb.title, kb.category, kb.view_count
ORDER BY ticket_to_view_ratio DESC;

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Step 3: Create MCP Tools with AI

• Prompt: “Create a tool to find topics with many support tickets but no knowledge base articles”
• AI generates: find_knowledge_gaps(min_tickets: number, days_back: number)

• Prompt: “Create a tool to suggest new knowledge base articles based on support ticket patterns”
• AI generates: suggest_articles(category: string, min_frequency: number)

• Prompt: “Create a tool to analyze how well articles are reducing support tickets”
• AI generates: analyze_article_effectiveness(article_id: string, days_back: number)

• Prompt: “Create a tool to search knowledge base articles and product documentation”
• AI generates: search_knowledge_base(query: string, category: string)

• Prompt: “Create a tool to retrieve FAQ answers from knowledge base”
• AI generates: get_faq_answer(question: string)

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Step 4: Test and Publish

1. Test each tool with sample queries
2. Verify knowledge gap identification
3. Validate article suggestions
4. Publish tools
5. Connect to agent builder

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Example Agent Interactions

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Scenario 1: Knowledge Gap Identification

1. Calls find_knowledge_gaps(5, 90) - finds topics with 5+ tickets in last 90 days
2. Analyzes results and responds:
   • “I found 12 topics that need articles:
     1. ‘Password Reset Issues’ - 23 tickets, avg resolution 2.5 hours
     2. ‘Payment Processing Errors’ - 18 tickets, avg resolution 4.2 hours
     3. …”

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Scenario 2: FAQ Answering

1. Calls search_knowledge_base("password reset", null)
2. Finds relevant article
3. Responds: “To reset your password: 1. Go to login page, 2. Click ‘Forgot Password’, 3. Enter your email, 4. Check your inbox for reset link…”

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Scenario 3: Article Effectiveness Analysis

1. Calls analyze_article_effectiveness(null, 30)
2. Analyzes results:
   • “Top performing articles: ‘Account Setup Guide’ has 0.5% ticket-to-view ratio, ‘Billing FAQ’ has 0.8% ratio…”

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Outcomes

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Knowledge Base Quality

• Article Coverage: 40% increase in topics covered
• Article Effectiveness: 35% reduction in related support tickets
• Update Frequency: 3x faster article updates
• Content Relevance: 50% improvement in article relevance scores

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Support Efficiency

• Self-Service Rate: 60% of common questions answered via knowledge base
• Ticket Reduction: 25% reduction in support tickets
• Resolution Time: 30% faster resolution for topics with articles
• Agent Productivity: More time for complex issues

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Data-Driven Insights

• Gap Identification: Automated identification of knowledge gaps
• Content Strategy: Data-driven article prioritization
• Effectiveness Tracking: Continuous monitoring of article performance
• Trend Analysis: Identification of emerging support patterns

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Best Practices

1. Regular Updates: Schedule weekly knowledge gap analysis
2. Content Quality: Review and update articles based on effectiveness metrics
3. User Feedback: Incorporate user feedback into article improvements
4. Categorization: Maintain clear article categories for better search
5. Analytics: Use Evals to track agent knowledge base usage patterns

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Next Steps

• Customer Support Agent Example - Build a support agent
• Product Feedback Analyzer Example - Analyze user feedback
• Content Performance Analyzer Example - Analyze content effectiveness