Product Manager Jobs at Decagon

Decagon PM interview guide

Decagon is a fast-growing conversational AI startup building AI agents for customer support, used by enterprise brands. Two distinct PM tracks appear in interview reports: the platform Product Manager (owns a major product area of the AI agent platform: agent building, testing, experimentation, analytics, QA, versioning, integrations, enterprise controls) and the Agent Product Manager (a hybrid PM/forward-deployed/consultant role that owns the full agent lifecycle for customer brands, writes and tests prompt logic, and shapes a customer's AI roadmap). PUBLIC SOURCING IS THIN: across Glassdoor, Blind, Exponent, InterviewQuery, Prepfully, 1Point3Acres, and interviewdb, almost all documented question text is for the Software Engineer loop (coding + ML + system design), not PM. There is no public bank of verbatim Decagon PM questions. What IS reliably sourced is the PM/Agent PM interview PROCESS and a handful of reported question THEMES (a real-customer take-home case study, behavioral rounds heavy on AI-tool fluency, values alignment, and translating product strategy into engineering specs). The questions below are reconstructed from those sourced themes and the published role bar; treat them as representative of what is reported, not as transcribed verbatim questions (which Decagon does not publicly expose for PM). Glassdoor ratings: PM rated ~2/5 difficulty (50% positive); Agent PM / APM rated the hardest of all Decagon roles. Average process length ~12 days. Comp reported ~$232k-$290k base with equity. Company values to align answers to: Just Get It Done, Invent What Customers Want, Winner's Mindset, The Polymath Principle.

The interview process

1. Recruiter / hiring-manager screen (30-45 min). Background, motivation, and role-fit discussion. Reported across Glassdoor and 1Point3Acres as the opening stage. For the platform PM track, candidates report it skews lighter; for Agent PM / APM it is reportedly harder and more probing.
2. Take-home case study on a real customer case. The most consistently reported PM-specific stage. Candidates are given a take-home exercise to solve a REAL customer case (described as realistic and manageable for the role), or a case study to write up as a proposal. This maps directly to Decagon's business: building, testing, and scaling AI agents for an enterprise customer.
3. Live case review with Agent PMs. Candidates present and defend the take-home proposal live with other Agent PMs (or present the written proposal). Tests structured thinking, prioritization, and ability to translate customer needs into a buildable plan.
4. Behavioral / values rounds. Reported as mostly behavioral with heavy emphasis on AI-tool fluency and willingness to upskill with AI. Answers are expected to align with Decagon's values (Just Get It Done, Invent What Customers Want, Winner's Mindset, The Polymath Principle). Includes leadership, cross-functional collaboration, and handling stakeholder feedback.
5. Technical depth / engineering-collaboration check. Per the role bar and reported prep notes, expect to discuss APIs, data models, and architecture comfortably with engineers, and how you translate ideas into specs. Agent PM specifically tests writing/testing prompt logic and shaping AI agent designs.
6. On-site (San Francisco) / final + CEO. Reported final stage is an on-site in SF; some candidates report a call with the CEO before the offer. Then references and onboarding.

Likely PM interview questions

1. Walk us through a take-home case for a real Decagon customer: how would you scope, build, test, and launch an AI support agent for them, and how would you sequence the work?Product sense
2. Given a customer case study, write up a product proposal and present it: what is the problem, your recommended solution, and why?Execution
3. How would you guide a customer's executive team through the shift from managing a large human support workforce to optimizing content and processes for AI agents?Strategy