Metrics question
Duet promises two outcomes: better agent performance and a faster improvement loop. What metric stack would you define to measure both, from user-visible business impact to product adoption to operational latency? Be explicit about leading vs. lagging indicators, the unit of analysis, and how you’d avoid false positives where Duet generates lots of suggestions but doesn’t materially improve production outcomes.
- Decagon
- Metrics
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Whether you can build a full metric stack connecting business outcome to operational detail, and specifically avoid the false positive of high suggestion volume masking low real impact.
How to approach it
- Define the unit of analysis first: a single suggestion, a customer account, or an agent workflow, since the same volume of suggestions means different things at each level.
- Business impact layer (lagging): change in agent resolution rate or CSAT after fixes are applied, measured per customer over a rolling window.
- Adoption layer: percentage of suggestions reviewed and accepted, not just generated, since generation alone is a vanity metric.
- Operational layer (leading): time from failure detection to suggestion delivered, and time from suggestion to applied fix, since Duet's pitch is a faster improvement loop.
- Guard against the false positive explicitly: track accepted-but-ineffective fixes, where an accepted suggestion did not move the downstream business metric, and treat a rising rate of those as a red flag.
What a strong answer includes
- Names the false-positive guard explicitly, accepted suggestions that do not move outcomes, which is exactly what the question asks to avoid.
- Separates leading (loop speed) from lagging (business outcome) indicators clearly instead of mixing them.
- Picks resolution rate or CSAT, real business metrics, over suggestion count as the ultimate bar.
Common mistakes
- Uses suggestion volume as a proxy for value without checking downstream impact.
- No defined unit of analysis, making the metrics hard to compare across customers of different sizes.
Likely follow-up questions
- How would you attribute a resolution rate change to Duet versus other concurrent changes.
- What time window would you use to judge whether a fix actually worked.
More metrics questions
- 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
- 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
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