Metrics question
A newly launched enterprise AI agent is live in production. What north-star and guardrail metrics would you track across automation, resolution quality, customer experience, and business impact, and how would you use those metrics to decide whether to improve workflows, add integrations, tighten scope, or retrain operational processes around the agent?
- Decagon
- Metrics
- Hard
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What this question tests
Tests the ability to design a metric stack for a live enterprise AI agent that balances automation with safety and business value.
How to approach it
- Pick a north star tied to business value the agent was bought to deliver, such as resolved tickets or hours saved per week.
- Track automation metrics like containment rate and self serve resolution rate by intent.
- Track quality metrics like factual accuracy, policy adherence, and escalation appropriateness, sampled by human review.
- Track guardrails like critical error rate, unsafe action rate, and customer complaint rate that can pause rollout.
- Track business impact like cost per resolution, NPS or CSAT delta, and renewal or expansion signal.
- Use guardrail breaches to tighten scope or add human in the loop, and use plateaued metrics to trigger new integrations or retraining.
What a strong answer includes
- Distinguishes leading indicators, like containment, from lagging ones, like renewal, so decisions do not wait months for signal.
- Sets explicit thresholds, for example critical error rate above 1 percent pauses expansion, rather than reviewing metrics ad hoc.
- Ties each metric to a specific action, so a metric that cannot change a decision is cut.
Common mistakes
- Optimizing only for containment while critical error rate silently rises.
- Picking a north star that is easy to measure but not what the customer actually bought.
Likely follow-up questions
- How would you set the threshold for a critical error rate guardrail?
- What would you do if containment is high but CSAT is falling?
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