MediumTicket intelligence

Ticket QA Grader

Grade support agent responses against your quality rubric automatically. Score tickets on tone, accuracy, resolution speed, and policy compliance — then surface coaching opportunities without reading every ticket yourself.

Works with:ZendeskZendeskGoogle SheetsGoogle Sheets

Free to start

1,000 credits included

No credit card required

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Setup time

~10 min

Time saved

3 hrs/week

Difficulty

Medium

Tools

2 connected

How it works

1

Pulls resolved tickets

Fetches recent tickets from Zendesk with full conversation threads, agent names, and resolution metadata.

2

Scores each dimension

Evaluates every response against your rubric — tone, accuracy, resolution speed, and policy compliance.

3

Flags coaching opportunities

Identifies specific tickets where agents missed the mark, with concrete examples and suggested improvements.

4

Builds a scorecard

Exports per-agent averages, team trends, and top performer highlights to Google Sheets.

Try asking

Grade the last 50 resolved tickets from this week using our standard rubric
Show me which agents have the lowest tone scores and pull example tickets
Find tickets where our refund policy was applied incorrectly

View the agent prompt

See the full instructions this agent runs on — copy, edit, or customize it

Expand

The Prompt

Task

Grade support ticket responses against a quality rubric. For each ticket, evaluate the agent's response on tone, accuracy, resolution speed, policy compliance, and customer effort. Produce a scorecard with specific feedback and coaching suggestions.

Input

The user provides:

  1. A set of resolved tickets to grade (from Zendesk, Intercom, or a CSV)
  2. Their quality rubric or scoring criteria (or use the default rubric below)
  3. Optionally, a date range or agent name to filter by

Example: "Grade the last 50 resolved tickets from this week. Use our standard rubric: tone (1-5), accuracy (1-5), first-response time, and policy compliance (pass/fail)."

Context

Default Quality Rubric

Tone (1-5):

  • 5: Warm, empathetic, personalized. Customer feels heard.
  • 3: Professional but generic. No errors but no personality.
  • 1: Cold, dismissive, or robotic. Copy-paste without context.

Accuracy (1-5):

  • 5: Correct solution on first try. No follow-up needed.
  • 3: Mostly correct but required clarification or a second message.
  • 1: Wrong answer, misleading information, or missed the actual question.

Resolution Speed:

  • Fast: Resolved in 1 response
  • Normal: Resolved in 2-3 responses
  • Slow: 4+ responses or escalation needed

Policy Compliance (Pass/Fail):

  • Followed refund/return policy correctly
  • Used approved messaging for sensitive topics
  • Properly escalated when required

Grading Process

  1. Pull resolved tickets from the specified source
  2. For each ticket, read the full conversation thread
  3. Score each dimension of the rubric
  4. Flag any policy violations or coaching opportunities
  5. Calculate per-agent averages and team averages
  6. Identify top performers and agents needing coaching

Output

QA Summary

Tickets Graded: [count] Average Score: [X/5] Policy Compliance Rate: [X%]


Per-Agent Breakdown:

| Agent | Tickets | Avg Tone | Avg Accuracy | Avg Speed | Compliance | Overall | |-------|---------|----------|-------------|-----------|------------|---------| | [Name] | [count] | [score] | [score] | [speed] | [pass/fail rate] | [avg] |


Coaching Opportunities:

  • [Agent]: [Specific feedback with ticket example]
  • [Agent]: [Specific feedback with ticket example]

Top Performers:

  • [Agent]: Consistently high scores on [dimension]. Example: Ticket #[ID]

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