# Decagon Interview Guide (2026): AI-Powered Customer Support

Source: https://www.techinterview.org/companies/decagon-interview-guide/
Updated: 2026-07-03 · techinterview.org

**TL;DR —** Decagon interviews focus on whether you can build and ship AI agents for customer support, testing practical engineering and product judgment over textbook algorithm puzzles. Expect a recruiter screen, one or more technical or role-specific rounds, and a final onsite, with engineers facing coding plus LLM-oriented system design and other roles assessed on customer empathy and execution speed. Preparation pays off most when you can show real problem-solving, familiarity with conversational AI products, and that you do well in a fast-moving startup.

Decagon is one of the leading AI-native customer support platforms — autonomous agents that resolve tickets end-to-end at companies like Notion, Eventbrite, and Bilt. Founded by ex-OpenAI engineers. The interview is selective and reflects the high engineering bar of frontier AI startups.

## Process

Recruiter screen → 60-minute [coding pair](/algorithm-patterns-cheat-sheet/) → 60-minute [system design](/category/system-design/) → 60-minute past-project deep dive → [behavioral](/post/3233460379/behavioral-interview-questions-2026-star-method-amazon-leadership-principles-and-winning-answers/). Cycle: 3–4 weeks.

## What they actually ask

- Design an AI agent system that resolves customer support tickets autonomously — focus on the full loop: reading intent, calling backend tools (refunds, order lookups, account changes), and deciding when to escalate to a human. Interviewers push on guardrails, meaning how you stop the agent from taking a wrong or irreversible action and how it behaves on ambiguous or out-of-scope requests.

- Design knowledge ingestion and retrieval for company-specific support documents — walk through the pipeline: chunking help-center articles and past tickets, generating embeddings, and running vector search, plus keeping the index current as docs change. Expect questions on grounding every answer in retrieved sources so the agent doesn't invent policies, and on handling stale or conflicting documents.

- Design evaluation framework that measures autonomous resolution quality — start by defining what "resolved" means (problem actually solved versus ticket merely deflected), then cover offline test sets built from past tickets, LLM-as-judge scoring, human review, and live metrics like resolution rate, escalation rate, and CSAT. A common probe is how you catch quality regressions before a prompt or model change ships.

- Coding: practical TypeScript/Python, often with concurrency or LLM-tooling flavor — expect to build something that works rather than a puzzle, such as parsing a tool-call schema, handling a streaming response, retrying failed API calls with backoff, or coordinating concurrent requests. Clean, well-structured code and sensible error handling count more than a clever trick.

- Past-project deep dive: must demonstrate genuine engineering depth — pick a project you personally drove and can go several layers deep on, including the hard trade-offs you made, why you rejected the alternatives, and exactly what you built versus what the team did. Interviewers keep drilling until they reach the edge of what you understand, so hand-wavy ownership gets caught quickly.

## Levels and comp (2026)

- SE: $200K–$260K [total](/total-comp-calculator/)

- Senior SE: $290K–$390K

- Staff: $420K–$560K

- Principal: $580K–$770K

Equity has high upside given Series B/C valuation and category leadership.

## Prep priorities

- Be fluent in TypeScript/Python and at least one LLM API — you should be able to define a tool/function schema, parse the model's structured output, and handle a streaming response without stopping to look things up.

- Understand RAG, agent loops, and eval frameworks — be ready to explain a ReAct-style loop (reason, call a tool, observe the result, repeat), when retrieval helps versus adds noise, and how you would score outputs offline.

- Read papers and engineering posts on agent design (Anthropic, OpenAI cookbooks) — pay attention to the failure modes they cover, such as prompt injection, tool misuse, and runaway loops, and the mitigations teams put in place.

## Frequently Asked Questions

### Is Decagon remote-friendly?

Hybrid in San Francisco; some remote within US for senior roles.

### How does Decagon compare to Sierra, Ada, or Forethought?

Sierra is the most prominent rival; Ada is the older incumbent; Forethought has been pivoting. Decagon and Sierra are the highest-comp; Decagon's technical bar is famously demanding.

### What is the engineering bar?

Very high. The team is small and the standards reflect frontier AI lab norms. Strong technical writing, demonstrable craft, and AI-native fluency are essential.

## Compensation and How to Think About the Equity

An offer here comes as cash plus equity, and the two work very differently. At an early-stage company the base salary often sits below what the largest tech firms pay, with the difference meant to be covered by the equity grant. Pay ranges move quickly, so confirm current numbers against a recent offer or a source like levels.fyi rather than trusting any figure you read in a guide. Treat the base as the part you can count on and the equity as a separate, much less certain bet.

Most grants at this stage are stock options, not RSUs. An option gives you the right to buy shares at a fixed strike price set on the day of the grant, so you only make money if the company is later worth more than that strike; RSUs, common at public companies, are shares handed to you outright as they vest. Options typically vest over four years with a one-year cliff, meaning you walk away with nothing if you leave in the first twelve months. Two facts get glossed over in offer conversations. Your ownership percentage shrinks as later funding rounds issue new shares, which is dilution, and the shares are illiquid: you usually cannot sell until an acquisition or IPO, which may be years out or may never happen. The downside is real, the equity can go to zero, and so is the upside, a grant that becomes worth several times your cash if the company does well.

|  | Big-tech | Early-stage like Decagon |
| --- | --- | --- |
| Base salary | Higher, predictable | Often lower |
| Equity type | RSUs, liquid as they vest | Options with a strike price |
| Liquidity | Sell on a set schedule | Locked until a liquidity event |
| Range of outcomes | Low variance | Wide, can be zero or large |

When you negotiate, separate the things you can actually move. Trading cash for more equity, or the reverse, depends on whether you need income now or believe in the long-term outcome, so decide that before you sit down. The initial grant loses value as it vests and as new rounds dilute you, which makes the size and cadence of refresh grants worth as much attention as the headline number. The cliff is sometimes negotiable for senior hires. Ask for the strike price, the most recent 409A valuation, the total shares outstanding so you can compute your real percentage rather than a share count that means nothing on its own, and what happens to vested and unvested options if you leave or the company is acquired. Get the answers in writing.