AI & Technical question
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
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
Tests structuring a rigorous technical proof process for high-volume, multi-channel AI automation, covering pilot scope, evals, guardrails, and escalation design, then translating results into a signable scope.
How to approach it
- Choose the initial pilot workflows from the highest-volume, most templated interaction types, likely chat first since it's easiest to evaluate and has clear escalation paths, deferring voice given its added complexity.
- Define the eval suite before the pilot: accuracy on intent resolution, appropriate escalation rate, and customer satisfaction compared against the human baseline for the same ticket types.
- Design guardrails: confidence thresholds that trigger automatic human handoff, and hard exclusions for sensitive or high-risk interaction types that should never be automated in the pilot.
- Design human-escalation UX so handoffs feel seamless to the end customer, with full context passed to the human agent, not a cold transfer.
- Run the pilot with a defined volume and duration, and review results jointly with the customer against the pre-agreed eval thresholds.
- Translate pilot results into a signed scope: which workflows and channels graduate to production, which need more tuning, and a phased plan to add voice, email, and SMS.
What a strong answer includes
- Chooses chat first explicitly because it's the easiest to evaluate and has the clearest handoff paths, sequencing complexity deliberately rather than trying all channels at once.
- Defines the eval suite and guardrail thresholds before the pilot starts, so results can't be disputed or moved after the fact.
- Designs escalation as a first-class part of the proof, since a clean handoff is often what convinces a skeptical buyer more than raw automation rate.
- Converts pilot results directly into a scoped, phased contract, rather than a vague we proved it works pitch.
Common mistakes
- Trying to prove safety across all four channels at once instead of sequencing by complexity.
- Defining evals or guardrail thresholds only after seeing results, undermining the credibility of the proof.
- Designing escalation as an afterthought, leaving customers with a jarring cold handoff experience during the pilot.
Likely follow-up questions
- What would you do if pilot results are strong for chat but weak for voice?
- How would you set the confidence threshold for automatic escalation?
More ai & technical questions
- 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
- 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