AI & Technical question
How would you design an evaluation framework for Duet that measures two things separately: whether it correctly identifies agent failures in production conversations, and whether its proposed fixes are actually high quality before they reach production? Define the labels, offline and online evals, human review criteria, and failure modes you’d want the team to track.
- Decagon
- AI & Technical
- 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 design an eval framework that separates two distinct quality questions, detection accuracy and fix quality, since conflating them hides where the product is actually failing.
How to approach it
- Split the problem explicitly: detection (did Duet correctly flag that the agent failed) and remediation (is the proposed fix actually good) are different tasks needing different evals.
- For detection, define labels like true failure, false positive, and missed failure, built from a human-reviewed sample of production conversations.
- For fix quality, define labels like fix resolves the root cause, fix is superficial, and fix introduces a new problem, reviewed by someone who understands the customer's agent design.
- Build offline evals against a held-out labeled set for both dimensions, and online evals tracking whether customers accept, edit, or reject each proposed fix in production.
- Track failure modes separately per dimension, like missed failures in ambiguous multi-turn conversations, versus fixes that work in isolation but break a different flow.
What a strong answer includes
- Keeps detection and remediation as genuinely separate eval tracks with separate labels and separate metrics, matching what the question asks.
- Defines specific label categories instead of a generic pass or fail scale.
- Uses production acceptance or edit rate as the online signal for fix quality, which is more meaningful than an internal quality score alone.
Common mistakes
- Merges detection and fix quality into one combined accuracy number, losing the diagnostic value.
- No plan for who reviews fix quality, when it requires domain understanding of the customer's own agent.
Likely follow-up questions
- How would you handle a fix that looks correct offline but customers still reject.
- What sample size and review cadence would you use for the human-labeled set.
More ai & technical questions
- During pre-sales for a Fortune 500 support organization, the buyer believes Decagon’s AI agent vision is compelling but doubts it can safely automate high-volume interactions across chat, email, SMS, and voice. How would you structure the technical proof process: which workflows would you include in the initial pilot, what evals, guardrails, and human-escalation design would you use to demonstrate safety and quality, and how would you translate the results into a scope the customer is willing to sign?Decagon · AI & Technical · Hard
- Decagon wants enterprises to control how agents change in production. Define the product requirements for versioning, approvals, rollout/rollback, evaluation gates, and auditability. Then describe the minimum API surface, core data model, and architecture choices you would align on with engineering to support safe agent updates at enterprise scale.Decagon · AI & Technical · Hard
- Design a product that analyzes conversations across voice, chat, email, and SMS and gives support teams actionable recommendations (for example, workflow fixes, agent behavior changes, or knowledge-base gaps). What jobs-to-be-done would you prioritize first, what should the UI show so teams trust and act on the recommendations, and how would you handle confidence, evidence/citations, and feedback loops to improve recommendation quality over time?Decagon · AI & Technical · Hard
- A Fortune 500 prospect wants to automate high-value support interactions across chat, email, SMS, and voice, but the C-suite doubts an AI agent can do this safely. How would you structure a pre-sales pilot: which workflows would you start with, what model failure modes and guardrails would you test, what offline and online evals and success thresholds would you require, and how would those results translate into a full production rollout?Decagon · AI & Technical · Hard
- One of Decagon's largest customers has an agent live, but adoption has plateaued: simple cases are automated well, while high-value complex cases still escalate to humans. How would you diagnose whether the main bottleneck is workflow design, knowledge retrieval, tool/API reliability, policy ambiguity, or model behavior, and how would you prioritize the next set of improvements?Decagon · AI & Technical · Hard
- An enterprise agent is live for a large customer, and containment is improving but CSAT is flat. How would you identify the highest-priority workflow failures? Specify the telemetry you would use, how you would separate model-quality issues from workflow or integration issues, and how you would direct Forward Deployed Engineers on the order of fixes.Decagon · AI & Technical · Hard
More questions from Decagon
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture