Support Triage Agent: Faster Prioritization and Issue Summaries

Create an agent that helps your support team triage tickets faster by pulling customer context, summarizing issues, and flagging high-priority cases automatically.

Why Build a Support Triage Agent?

Most support teams spend too much time manually reviewing tickets, checking customer context, and deciding which tickets need immediate attention. In this guide, we'll build a support triage agent that automatically pulls customer context, summarizes issues, and flags high-priority cases—so your team can focus on solving problems instead of sorting through tickets.

What This Agent Is Expected To Do

This agent answers concrete questions about support tickets and customer context. Here's what it should handle:

• "What's the priority of ticket #12345 and why?"
• "Give me a summary of all high-priority tickets from the last 24 hours"
• "What's the context for this customer's ticket? Show me their history"
• "Which tickets should I prioritize today?"
• "Summarize the issue in ticket #67890 and suggest a resolution"
• "Which tickets are similar to this one and how were they resolved?"

The agent doesn't just return ticket data—it provides context, explains priorities, and surfaces actionable insights.

Who This Agent Is For

This agent is built for teams that need faster ticket triage and better customer context:

• Support Agents – Quick customer context, issue summaries, priority guidance without switching tools
• Support Managers – Ticket prioritization, workload distribution, team performance insights
• Support Ops – Triage automation, routing logic, SLA tracking, resolution patterns
• Customer Success – Escalation context, customer health signals, proactive support

If you live in support ticketing systems, CRM, product usage data, and billing systems, this agent is for you.

Core Use Cases

Here are the scenarios where this agent shines:

Faster ticket triage: Instead of spending 5 minutes reading through a ticket and checking customer history, the agent gives you a complete summary: customer context, issue description, priority level, similar tickets, and suggested resolution—in one question.

Automatic priority flagging: The agent identifies high-priority tickets based on customer value, issue severity, system status, and similar ticket patterns. You can focus on what matters most.

Customer context on demand: When a ticket comes in, the agent provides complete customer context: account status, recent activity, previous tickets, product usage, billing status. No more switching between tools.

Issue summarization: The agent reads ticket descriptions, identifies the core issue, and suggests resolutions based on similar resolved tickets. New agents can resolve issues faster.

The Ingredients: Data You'll Need

To build this agent, you'll need data from several systems:

Support ticket data (from Zendesk, Intercom, etc.):

• Tickets: ticket ID, subject, description, status, priority, created date, assignee
• Customer: customer email, customer name, account ID
• Interactions: comments, replies, internal notes

Customer data (from CRM, billing):

• Account: account name, plan tier, MRR, account status
• Billing: payment status, subscription status, renewal date
• History: previous tickets, resolution history, satisfaction scores

Product usage data (from product analytics):

• Usage: active users, feature adoption, last seen date
• Events: error events, feature usage, login patterns
• Health: usage trends, engagement scores

System status data (from monitoring):

• System status: service health, incident status, affected systems
• Incidents: active incidents, resolution status, impact

Derived fields (calculated in the view):

• customer_priority_score: Calculated priority based on account value and history
• issue_severity: Severity based on keywords and system status
• similar_ticket_count: Number of similar resolved tickets
• avg_resolution_time: Average resolution time for similar tickets
• suggested_priority: Recommended priority level
• estimated_resolution_time: Estimated time to resolve based on similar tickets

Here's an example SQL view:

-- Support Triage View
SELECT 
  -- Ticket data from support system
  t.ticket_id,
  t.subject,
  t.description,
  t.status,
  t.priority as original_priority,
  t.created_date,
  t.assigned_to,
  DATEDIFF(CURRENT_DATE(), t.created_date) as ticket_age_hours,
  
  -- Customer data from CRM
  c.account_name,
  c.plan_tier,
  c.mrr,
  c.account_status,
  
  -- Customer history
  COUNT(DISTINCT th.ticket_id) as previous_ticket_count,
  MAX(th.resolved_date) as last_ticket_date,
  AVG(th.resolution_time_hours) as avg_historical_resolution_time,
  AVG(th.satisfaction_score) as avg_satisfaction_score,
  
  -- Product usage context
  u.active_users,
  u.last_seen_date,
  DATEDIFF(CURRENT_DATE(), u.last_seen_date) as days_since_last_seen,
  COUNT(DISTINCT e.error_id) as error_count_7d,
  
  -- System status
  s.system_status,
  s.incident_status,
  
  -- Similar tickets analysis
  COUNT(DISTINCT st.ticket_id) as similar_ticket_count,
  AVG(st.resolution_time_hours) as avg_similar_resolution_time,
  MAX(st.resolution_summary) as common_resolution,
  
  -- Calculated priority
  CASE 
    WHEN c.mrr > 5000 THEN 'high_value'
    WHEN c.mrr > 1000 THEN 'medium_value'
    ELSE 'standard_value'
  END as customer_value_tier,
  CASE
    WHEN t.subject LIKE '%critical%' OR t.subject LIKE '%down%' OR t.subject LIKE '%urgent%' THEN 'high'
    WHEN t.subject LIKE '%error%' OR t.subject LIKE '%bug%' THEN 'medium'
    ELSE 'low'
  END as issue_severity,
  CASE
    WHEN c.mrr > 5000 AND (t.subject LIKE '%critical%' OR s.incident_status = 'active') THEN 'critical'
    WHEN c.mrr > 5000 OR t.subject LIKE '%critical%' OR s.incident_status = 'active' THEN 'high'
    WHEN c.mrr > 1000 AND (t.subject LIKE '%error%' OR error_count_7d > 5) THEN 'high'
    WHEN previous_ticket_count > 5 AND avg_satisfaction_score < 3 THEN 'high'
    ELSE 'medium'
  END as suggested_priority,
  CASE
    WHEN similar_ticket_count > 0 THEN avg_similar_resolution_time
    WHEN avg_historical_resolution_time IS NOT NULL THEN avg_historical_resolution_time
    ELSE 2.0
  END as estimated_resolution_hours
  
FROM zendesk.tickets t
LEFT JOIN hubspot.accounts c ON t.account_id = c.account_id
LEFT JOIN zendesk.ticket_history th ON c.account_id = th.account_id 
  AND th.resolved_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY)
LEFT JOIN product_analytics.users u ON c.account_id = u.account_id
LEFT JOIN product_analytics.error_events e ON c.account_id = e.account_id 
  AND e.event_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)
LEFT JOIN monitoring.system_status s ON s.affected_system = t.category
LEFT JOIN zendesk.similar_tickets st ON 
  (t.subject LIKE CONCAT('%', st.keywords, '%') OR t.description LIKE CONCAT('%', st.keywords, '%'))
  AND st.status = 'resolved'
WHERE t.status IN ('new', 'open', 'pending')
GROUP BY 
  t.ticket_id, t.subject, t.description, t.status, t.priority, t.created_date, t.assigned_to,
  c.account_name, c.plan_tier, c.mrr, c.account_status,
  u.active_users, u.last_seen_date,
  s.system_status, s.incident_status;

This view joins data from your support system (tickets), CRM (accounts), product analytics (usage, errors), and monitoring (system status), and calculates priority scores and resolution estimates.

Key points:

• The view joins across multiple systems in a single query
• It calculates derived fields (customer value tier, issue severity, suggested priority)
• It filters to only active tickets (new, open, pending)
• It uses time windows (last 7 days for errors, last 90 days for history)

Step 4: Add MCP Tools on Top of This View

Create MCP tools that expose this view to agents. Tools translate natural language questions into SQL queries.

Tool 1: triage_ticket(ticket_id: string)

This tool returns complete triage information for a specific ticket.

Description: "Returns comprehensive triage information for a ticket including customer context, priority analysis, similar tickets, and suggested resolution."

SQL Query:

SELECT * FROM support_triage_view
WHERE ticket_id = {ticket_id};

Tool 2: list_high_priority_tickets(limit: number, hours: number)

This tool lists high-priority tickets from the last N hours.

Description: "Returns list of high-priority tickets (critical or high suggested priority) from the last N hours, sorted by priority and ticket age."

SQL Query:

SELECT * FROM support_triage_view
WHERE suggested_priority IN ('critical', 'high')
  AND created_date >= DATE_SUB(CURRENT_DATE(), INTERVAL {hours} HOUR)
ORDER BY 
  CASE suggested_priority 
    WHEN 'critical' THEN 1 
    WHEN 'high' THEN 2 
  END,
  ticket_age_hours ASC
LIMIT {limit};

Tool 3: get_customer_ticket_context(customer_email: string)

This tool returns complete customer context for support tickets.

Description: "Returns customer context including account information, recent tickets, product usage, and billing status for support triage."

SQL Query:

SELECT 
  account_name,
  plan_tier,
  mrr,
  account_status,
  previous_ticket_count,
  last_ticket_date,
  avg_historical_resolution_time,
  avg_satisfaction_score,
  active_users,
  days_since_last_seen,
  error_count_7d,
  customer_value_tier
FROM support_triage_view
WHERE customer_email = {customer_email}
GROUP BY 
  account_name, plan_tier, mrr, account_status,
  previous_ticket_count, last_ticket_date,
  avg_historical_resolution_time, avg_satisfaction_score,
  active_users, days_since_last_seen, error_count_7d, customer_value_tier;

Tools can be called by agents with natural language

Step 5: Wire the Tool to an Agent Builder

Connect your Pylar tools to an agent builder. You can use:

• OpenAI Platform – Build custom agents with GPT-4
• LangGraph – Build complex agent workflows
• Claude Desktop – Quick setup for internal use
• Cursor – Developer-focused agent builder
• Zapier / Make / n8n – Automation platforms

Agent Instructions:

Give your agent clear instructions.

You are a support triage assistant. Your job is to help support teams prioritize tickets, understand customer context, and summarize issues.

Rules:

1. Only use the tools provided. Do not invent ticket information or make assumptions.
2. When asked about a specific ticket, use triage_ticket to get complete context.
3. When asked about high-priority tickets, use list_high_priority_tickets with appropriate time windows.
4. When asked about customer context, use get_customer_ticket_context to get account and history information.
5. Always provide context: explain why a ticket is high priority, what the customer's history shows, or what similar tickets suggest.
6. If you don't have data, say so. Don't guess.

Available tools:

• triage_ticket(ticket_id) — Get complete triage information for a ticket
• list_high_priority_tickets(limit, hours) — List high-priority tickets
• get_customer_ticket_context(customer_email) — Get customer context for support

Example Prompts Users Can Try:

1. "What's the priority of ticket #12345 and why?"
2. "Give me a summary of all high-priority tickets from the last 24 hours"
3. "What's the context for this customer's ticket? Show me their history"
4. "Which tickets should I prioritize today?"
5. "Summarize the issue in ticket #67890 and suggest a resolution"
6. "Which tickets are similar to this one and how were they resolved?"

The agent uses the tools to answer these questions, providing context and actionable insights.

Step 6: Add Evals & Guardrails

Add observability and guardrails to ensure the agent works correctly and safely.

Logging:

Track:

• Which tickets and customers are queried most frequently
• Error rates and types
• Query latency
• Unusual outputs or patterns

Use Pylar's Evals system to monitor:

• Success rates: How often queries succeed vs. fail
• Error patterns: What errors occur and why
• Query shapes: What types of queries agents make
• Raw logs: Every query with full context

Guardrails:

Add simple checks:

No PII leaking: Views exclude sensitive data (personal notes, internal comments, payment details). Agents can only query through views, so they never see PII.

Row limits: Views automatically limit results (e.g., top 100 tickets, last 90 days of history). Agents can't query unbounded datasets.

Time windows: Views filter to recent data (last 7 days for errors, last 90 days for ticket history). Agents can't query historical data beyond what's needed.

Access control: Different views for different teams. Support agents see only their assigned tickets. Managers see team tickets. Support Ops sees all tickets.

Step 7: Rollout Playbook

How to introduce this agent to your support team:

Week 1: Pilot with 2–3 agents

• Set up the agent with basic tools
• Have agents try it for ticket triage
• Collect feedback on what's missing

Week 2: Expand to full team

• Add more tools based on feedback
• Create team-wide Slack integration
• Share example prompts

Week 3: Add automation

• Set up automatic priority flagging
• Create routing rules based on agent suggestions
• Set up alerts for critical tickets

Example Slack message: "When a new ticket comes in, ask the triage agent: 'What's the priority of ticket #[id] and why?'"

How This Replaces Dashboards (Without Killing Them)

Old flow: Open support system, read ticket, check customer in CRM, check product usage, check system status, manually decide priority, assign ticket.

New flow: Ask one question, get complete triage information with customer context, priority analysis, similar tickets, and suggested resolution—all in one answer.

The agent synthesizes data from multiple systems into a coherent narrative. You get answers in seconds instead of minutes.

Dashboards are still useful for:

• Monitoring known metrics
• Executive summaries
• Scheduled reports
• Visual exploration

Agents are better for:

• Ad-hoc questions
• Cross-system analysis
• Deep dives
• Proactive insights

Use both. Dashboards for monitoring. Agents for exploration.

Most support teams spend too much time manually reviewing tickets and checking customer context. This agent automatically pulls customer context, summarizes issues, and flags high-priority cases—so your team can focus on solving problems.

If you want to try this with your own data, you can spin up a Support Triage View in Pylar and plug it into your existing agent builder. Start with support ticket data, add CRM and product usage data as you connect them, and iterate based on what your team needs.