Metrics question
A large customer has an AI agent live in production, but adoption is below plan and leadership is hesitating on expansion. What metrics would you review first, how would you isolate whether the issue is workflow selection, agent quality, operational rollout, or stakeholder buy-in, and what actions would you take in the next 30 days to improve adoption and demonstrate business impact?
- Decagon
- Metrics
- Hard
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What this question tests
Tests root-causing weak adoption of a live production agent across four plausible failure modes, and turning the diagnosis into a concrete 30-day action plan.
How to approach it
- Review resolution rate, escalation rate, containment rate, and ticket volume routed to the agent versus total eligible volume, to see if the agent is underused or underperforming.
- Check workflow selection: is the automated ticket type actually high enough volume and value to matter, or was a low-impact workflow chosen initially.
- Check agent quality: compare resolution and satisfaction rates for automated tickets against the human baseline for the same ticket type.
- Check operational rollout: are agents routed correctly, is staff aware of and trusting the tool, are there manual overrides suppressing usage.
- Check stakeholder buy-in: interview the VP sponsor and frontline leads for hesitation reasons, which may be political or trust-based rather than technical.
- In the next 30 days, run the one or two highest-likelihood fixes, for example widening ticket routing or fixing a specific failure pattern, and re-measure resolution and containment weekly.
What a strong answer includes
- Separates the four failure modes with a specific metric or check for each, instead of guessing at one root cause.
- Compares agent performance directly against the human baseline to isolate quality from rollout issues.
- Treats stakeholder hesitation as a real, distinct failure mode requiring conversations, not just more engineering.
- Proposes a tight 30-day loop of fix and re-measure rather than a long diagnostic phase with no action.
Common mistakes
- Assuming low adoption is a quality problem without checking whether ticket routing or workflow selection is the actual cause.
- Ignoring stakeholder trust and political hesitation as a legitimate blocker to expansion.
- Taking 30 days purely to diagnose without shipping any fix or re-measuring impact.
Likely follow-up questions
- How would you separate a trust problem from an actual quality problem?
- What would you tell leadership if none of the four causes explain the gap?
More metrics questions
- You have inherited a new strategic account and must choose the first customer-support workflows to automate in production. What prioritization framework would you use to decide where the agent goes live first, and which adoption, quality, and business metrics would you require before recommending expansion into additional workflows or channels?Decagon · Metrics · Hard
- A live enterprise agent is generating strong customer demand for expansion, but engineers report unresolved reliability gaps in the current design. How would you decide what to ship next, including what evidence or thresholds you would require to expand safely, what you would defer, and how you would manage the conversation with the customer’s leadership team and internal engineering partners?Decagon · Metrics · Hard
- How would you define a metrics framework for Decagon’s developer experience across APIs, SDKs, and headless deployments? Specify the leading and lagging metrics you’d track from integration start through production launch, and explain how those metrics would change your roadmap priorities.Decagon · Metrics · Hard
- One of Decagon's largest customers has launched an agent, but adoption has plateaued because internal teams will not let it handle higher-value interactions. How would you diagnose whether the bottleneck is model quality, workflow design, integration gaps, or change management, and how would you decide which intervention to make first?Decagon · Metrics · Hard
- You own a customer support agent from first production launch through enterprise-wide expansion. What success metrics would you track in the first 30-60 days versus six months later, and how would you balance business outcomes, customer experience, and operational reliability when those metrics conflict?Decagon · Metrics · Hard
- A newly launched enterprise agent has lower-than-expected adoption even though the pilot performed well. Walk through how you would diagnose the drop using funnel metrics such as routing, engagement, containment, handoff, CSAT, and resolution; separate product issues from change-management or workflow issues; and prioritize the first 2-3 changes needed to recover adoption and earn expansion.Decagon · Metrics · Hard
More questions from Decagon
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