Metrics question
Suppose you launch an agentic capability that automates a cross-team workflow, such as answering employee questions and then taking actions in ServiceNow or Zendesk. What leading and lagging metrics would you use to judge success, and how would you separate real customer value from curiosity-driven usage or low-quality automation?
- Glean
- Metrics
- Hard
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What this question tests
Whether you can define a metrics framework for an agentic automation that separates genuine value from novelty usage.
How to approach it
- Define leading metrics: task completion rate without human correction, time from question to resolved action, and percent of sessions that result in a real ticket action versus just an answer.
- Define lagging metrics: reduction in ticket volume handled manually, change in resolution time for the automated workflow category, and repeat usage by the same employee over weeks.
- Separate curiosity usage from real value by looking at task complexity and whether the same user returns for the same task type, not just total session count.
- Track a quality guardrail: rate of incorrect or reversed actions taken in ServiceNow or Zendesk, since a wrong automated action is worse than no action.
- Segment by use case, since answering a question and taking an action in a ticketing system carry very different risk and value profiles and need separate bars.
- Use a control comparison where possible, for example resolution time for automated versus manually-handled tickets of the same type, to isolate real impact.
What a strong answer includes
- Distinguishes one-time curious usage from repeat, task-completing usage as the core signal of real value.
- Names a concrete guardrail, incorrect or reversed automated actions, since action-taking agents carry real operational risk unlike pure Q&A.
- Proposes a same-ticket-type comparison between automated and manual resolution to isolate causal impact rather than just watching an aggregate trend.
- Separates leading (in-session completion, low correction rate) from lagging (ticket volume reduction, repeat usage) explicitly.
Common mistakes
- Treating raw usage or session count as success without checking for repeat, task-completing behavior.
- No guardrail metric for wrong or reversed actions, which matters more for an action-taking agent than a pure answer bot.
- Not separating answering-only usage from consequential action-taking usage, which need different metrics.
Likely follow-up questions
- How would you detect curiosity-driven usage in the data you have?
- What would you do if repeat usage is high but reversed-action rate is also creeping up?
- How would you isolate the automation's causal impact on resolution time?
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More questions from Glean
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop