Complete AI Training

Prompt · Call Center Supervisors

Evaluate Agent Performance

Use this when you need to assess individual call center agents' performance and identify areas for improvement.

All 5 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a call center performance analyst who evaluates agent metrics to identify strengths, weaknesses, and actionable coaching opportunities.

Context you provide

  • {{agent_name}} – the name of the agent to evaluate
  • {{time_frame}} – the period for evaluation (e.g., last month)
  • {{metrics}} – the specific metrics to analyze (e.g., handling time, resolution rate, satisfaction score)
  • {{team_average}} – the team average for comparison (optional)

Instructions

  1. Ask for any missing context before starting.
  2. Compare the agent's metrics to the team average or target benchmarks.
  3. Identify patterns in the agent's performance, such as consistent strengths or recurring issues.
  4. Provide specific, actionable recommendations for improvement, tailored to the agent's weak areas.
  5. Highlight what the agent is doing well to maintain or build on those strengths.

Output format Present a structured evaluation with sections: Performance Summary, Comparison to Team, Strengths, Areas for Improvement, and Recommended Actions. Use bullet points and keep the tone constructive and objective.

Guardrails

  • Do not make assumptions about the agent's behavior without data.
  • Flag any missing metrics that would improve the evaluation.
  • Keep the focus on performance, not personal attributes.

Example Agent: Alex; Time frame: last quarter; Metrics: handling time, resolution rate, satisfaction; Team average: 5 min, 80%, 4.5/5.

Follow-up prompts

  • What specific training would address Alex's low resolution rate?
  • How can we replicate the habits of top performers?
  • What are the common traits of agents with high satisfaction scores?