Decagon product manager interview questions
47 questions asked in Decagon product manager interviews: 3 product design, 19 strategy, 13 metrics, 4 behavioral, 8 AI & technical. Each has an answer guide, and you can practice any of them in a mock interview.
Practice one of these Decagon questions now. An AI interviewer asks one of these questions, follows up, and scores your answer.
Start a mock interview · Mock interview from a job description
Open PM roles at Decagon
Real job descriptions from our catalog. Each one has a mock interview built from the posting.
- Senior Agent Product ManagerBrazil
- Senior Agent Product ManagerLondon
- Senior Agent Product Manager - Spanish SpeakingNew York City
- Senior Agent Product Manager, HealthcareNew York City
- Product Manager, Enterprise Agent PlatformSan Francisco
- Product Manager, DuetSan Francisco
- Senior Agent Product ManagerAustralia
- Senior Agent Product ManagerSan Francisco
- Product ManagerSan Francisco
- Senior Agent Product ManagerNew York City
Product design questions (3)
- Design a self-serve integration platform for enterprise customers building AI agents across voice, chat, email, and SMS. What are the core product primitives you would include first, and what tradeoffs would you make to balance power for technical users with simplicity for enterprise teams managing setup, permissions, testing, and ongoing maintenance?Decagon · Product design · Hard
- A Fortune 500 health plan wants Decagon to automate benefits and eligibility plus prior-authorization status across voice and chat, but compliance, clinical, and security leaders are worried about PHI, consent, and unsafe responses. How would you scope a first production launch: which intents, channels, and member segments would you include or exclude; what escalation and approval rules would you set; and what evidence would you bring to get executive sign-off?Decagon · Product design · Hard
- A Fortune 500 prospect wants to automate customer support across chat, email, and voice, but the C-suite is skeptical about quality and brand risk. How would you choose the first 1-2 workflows, define the pilot scope, and set launch criteria so the deployment is compelling enough to win the deal, feasible to ship in 8-12 weeks, and expandable after launch?Decagon · Product design · Hard
Strategy questions (19)
- A Fortune 500 prospect believes in Decagon’s vision but is skeptical that an AI agent can safely automate complex support journeys across chat, voice, email, and SMS. How would you structure the pre-sales process from discovery through pilot to secure the technical win, convince the C-suite the rollout is worth the risk, and choose the narrow initial deployment scope that maximizes proof of value while minimizing implementation risk?Decagon · Strategy · Hard
- You inherit a newly signed enterprise account where support workflows are fragmented, requirements are unclear, and the VP of Support, Operations, and IT all want different things first. What roadmap would you set for the first 90 days, how would you prioritize which workflow to launch first, and what milestones would you use to keep the account moving toward a production go-live?Decagon · Strategy · Hard
- On flagship deployments, customers will ask for bespoke capabilities that could either stay custom or become part of Decagon’s core platform. What framework would you use to decide which requests to productize versus keep account-specific, and how would you balance customer impact, implementation cost, roadmap coherence, and reusability across future enterprise accounts?Decagon · Strategy · Hard
- After several flagship enterprise deployments, you notice each customer asks for different custom logic, integrations, and operating processes. How would you distinguish among (a) one-off account work, (b) reusable deployment playbooks, and (c) core product investments, and how would you feed those decisions into Product and Engineering so future deployments get faster without overfitting to a single customer?Decagon · Strategy · Hard
- A top bank wants to deploy a Decagon agent, but its security review blocks launch over data retention, data residency, and deployment constraints. As PM, how would you drive the decision process to unblock this customer while avoiding one-off work and turning the solution into reusable platform capabilities for future regulated enterprises?Decagon · Strategy · Hard
- Decagon can support enterprise deployments through native integrations, customer-built API/SDK integrations, or partner-built extensions. How would you prioritize which systems and deployment patterns to productize first? Walk through the decision framework you’d use, including customer demand, implementation cost, reusability across accounts, security/compliance blockers, and impact on time-to-launch.Decagon · Strategy · Hard
- On customer calls, support leaders optimize for faster resolution and lower escalations, while technical buyers care about traceability, approval gates, and version control. How would you shape Duet’s product narrative and roadmap so it wins both audiences without becoming an overbuilt enterprise platform?Decagon · Strategy · Hard
- You see the same integration or workflow issue across several enterprise deployments. How would you decide whether to handle it as a one-off custom solution, turn it into a repeatable implementation playbook, or advocate for a core product investment? Walk through the decision criteria you would use.Decagon · Strategy · Hard
- A customer's executives want an aggressive launch date for a complex agent, but Forward Deployed Engineers believe the full scope carries material integration and reliability risk. How would you re-scope the deployment, sequence milestones, and align customer and internal stakeholders so that you protect trust without slipping into an open-ended implementation?Decagon · Strategy · Hard
- You are the first PM for Duet and have a new capability idea from research. How would you take it from concept to customer adoption: deciding whether it is worth building, selecting pilot customers, defining launch criteria, partnering with GTM on positioning, and determining whether to scale, iterate, or kill it after launch?Decagon · Strategy · Hard
- A large enterprise customer says Duet’s suggested workflow updates look useful, but they do not trust the AI enough to approve changes at scale. How would you prioritize the roadmap to increase trust without turning Duet into a slow, manual review tool? Walk through the customer discovery you’d run, the tradeoffs you’d evaluate, and the first capabilities you’d ship.Decagon · Strategy · Hard
- Pick one core platform area Decagon PMs own, testing, analytics, integrations, or enterprise controls. You have two quarters and conflicting asks from a Fortune 500 customer and several fast-growing startups. How would you decide what to build first, what to defer, and what principles would you use to balance segment-specific requests against platform-wide leverage?Decagon · Strategy · Hard
- After several custom healthcare deployments, Decagon sees repeated patterns in benefits verification, scheduling, escalation rules, and compliance reviews. How would you decide which pieces should become reusable healthcare product or playbook assets versus remain customer-specific, and how should that decision feed the broader healthcare roadmap?Decagon · Strategy · Hard
- You are in a first meeting with a payer or provider C-suite team that wants 'AI for customer experience' but has not aligned on the highest-value workflow. How would you structure the conversation to identify the right initial use case, quantify value and risk, and leave with a credible 12-month transformation roadmap?Decagon · Strategy · Hard
- After several flagship deployments, customers are asking for overlapping but not identical capabilities. How would you decide which lessons should become reusable implementation playbooks, which should become configurable product features, and which should remain bespoke work? Explain the criteria you would use and how you would feed those decisions into Decagon's core roadmap.Decagon · Strategy · Hard
- A Fortune 500 prospect wants to deploy Decagon across chat, email, and voice, but their leadership team is skeptical about safety and workflow complexity. How would you scope a 6-8 week pilot: which workflow(s) would you start with, what would you exclude, what fallback and escalation rules would you set, and what evidence would you require before proposing a broader transformation roadmap?Decagon · Strategy · Hard
- Three flagship customers want similar support automations, but each needs different integrations, policies, and workflow variations. How would you decide what should become core product, what should be reusable configuration or playbooks, and what should remain one-off forward-deployed work? How would you structure the feedback loop with Sales, FDEs, and Product/Engineering so those decisions improve future deployments?Decagon · Strategy · Hard
- A senior customer executive demands a highly customized feature that would accelerate one account, but Decagon's Product and Engineering teams want to invest only in reusable platform capabilities. How would you decide whether to build bespoke, generalize now, or push back? Walk through your decision criteria, tradeoffs, and how you would preserve the executive relationship either way.Decagon · Strategy · Hard
- After a flagship enterprise deployment, how would you convert what you learned into reusable assets for Decagon? Describe a framework for deciding which lessons should become implementation playbooks, which should become product requirements, how you would validate that a pattern generalizes beyond one customer, and how you would feed it back into sales and future deployments.Decagon · Strategy · Hard
Metrics questions (13)
- 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
- 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
- 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
- Decagon launches a self-serve agent configuration flow with real-time AI guidance. What metric tree would you use from signup to steady-state usage, and which leading and lagging indicators would tell you the feature is driving durable customer value rather than just a one-time setup spike?Decagon · Metrics · Medium
- An AI agent for patient access and scheduling has high adoption, but resolution rate is below target because too many conversations escalate to human staff. How would you break down the funnel, determine whether the root cause is workflow design, knowledge gaps, policy boundaries, or user behavior, and prioritize the first three fixes? What leading and lagging metrics would you use to know the agent is becoming core infrastructure?Decagon · Metrics · Hard
- 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
- Decagon is considering investment in a new channel or partner ecosystem, such as a marketplace listing, co-sell partnership, or a new communications channel. How would you decide whether to invest, and what post-launch signals would tell you to double down, iterate, or stop?Decagon · Metrics · Hard
- You are launching an AI agent for a complex support workflow across voice, chat, and SMS. What north-star and guardrail metrics would you set for the first 90 days, how would those metrics differ by channel, and what thresholds would convince you the agent is ready to expand to additional channels, intents, or geographies?Decagon · Metrics · Hard
Behavioral questions (4)
- Tell me about a time you owned a high-stakes enterprise initiative without direct authority over the people doing the work. How did you set decision rights, drive alignment across executive stakeholders and technical teams, make tradeoffs under ambiguity, and keep yourself accountable for the outcome?Decagon · Behavioral · Hard
- Describe a time you had to choose between shipping a customer-specific solution to win or save a strategic enterprise account and protecting the scalability of the core product. What tradeoffs did you consider, what decision did you make independently, and how did you align executives and engineers?Decagon · Behavioral · Hard
- Tell me about a time you shipped a technically complex platform feature under high ambiguity. How did you turn vague customer input into a spec, what tradeoffs did you make with engineering, design, and sales, and what outcome showed you made the right call?Decagon · Behavioral · Medium
- Tell me about a time you had to change scope or launch criteria for a regulated product or deployment because customer value, timeline, and compliance risk conflicted. How did you make the decision independently, align cross-functional stakeholders, and what tradeoffs did you accept?Decagon · Behavioral · Hard
AI & Technical questions (8)
- 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
- 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
- A Fortune 500 prospect wants a German-language support agent across chat and email, but the CFO and COO are skeptical about accuracy, brand risk, and time to value. Walk me through how you would scope a 6- to 8-week pilot: which workflows you would include or exclude, what data and integrations you would require, what offline and live evals you would run to prove German quality and brand safety, what launch guardrails and fallback paths you would set, what success criteria you would commit to, and how you would design the pilot so a win expands naturally into a broader CX transformation.Decagon · AI & Technical · Hard
Learn what these questions test
Chapters of the AI PM course, built from 604 real PM job postings.
- Chapter 4: Discovery and strategy for AI products
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop