What to Expect in the Decagon System Design Interview

Expect a 60 minute design round shaped by Decagon's own product. Decagon builds AI agents that resolve customer support tickets on their own. Candidates report design questions that mirror this work. Reported topics include knowledge ingestion and retrieval for support documents, and an AI gateway with audit requirements. You will not get a generic social network question. You will get an agent-shaped problem, and product judgment is graded with the architecture.

The Question Types

Knowledge ingestion and retrieval. Design the pipeline that turns a company's help articles into answers. This uses embeddings and vector search. An embedding is a list of numbers that represents the meaning of a text. Vector search finds stored texts whose numbers are closest to a question. The hard parts are stale documents, conflicting documents, and updates.

The agent system itself. Design an agent that reads a ticket, finds the answer, and acts. Acting can mean issuing a refund or checking an order. The hard parts are grounding, permissions, and escalation to humans. Grounding means the agent only states what its sources support.

The AI gateway. One reported onsite question asked for a gateway with auditability. A gateway is one service through which all model requests pass. Auditability means any request can be traced and reviewed later. This tests rate limits, logging, cost control, and multi-tenant isolation. Multi-tenant means many customer companies share one system safely.

Evaluation and monitoring. Decagon sells reliability to enterprises, so measurement questions are fair game. How do you know the agent answered correctly? Expect to discuss evaluation sets, sampled human review, and real time rule checks.

What the Interviewer Grades

Practical judgment matters more than exotic components. State requirements first, including scale and failure cases. Name your trade-offs out loud. Connect choices to the customer: a wrong refund costs real money. Candidates report strong interviewers who push on depth, so defend each choice with a reason.

A Walkthrough: Design a Support Agent

Here is a high level plan for the signature question.

1. Requirements (5 minutes). Many business customers, each with private data. Channels: chat, email, and voice. Target: resolve most tickets without a human, safely.

2. Ingestion pipeline. Pull each customer's help articles and policies. Split them into chunks, embed them, and store them in a vector database. Re-index on every document change so answers never use stale policy.

3. The agent loop. A ticket arrives. The system retrieves the most relevant chunks. The model drafts an answer grounded in those chunks. If an action is needed, the agent calls an approved API, such as a refund endpoint. Every action checks the customer's permission rules first.

4. Guardrails and escalation. Low confidence answers route to a human agent. Certain topics, like legal threats, always escalate. Log every model input and output for audit.

5. Evaluation. Keep a test set of real resolved tickets per customer. Score new model versions against it before rollout. Sample live conversations for human review. Run real time checks against plain language rules, which is what Decagon's Watchtower product does.

6. Scale. Isolate each customer's data completely. Cache frequent answers. Queue spikes so the system degrades slowly, not suddenly.

Common Mistakes in This Round

  • Starting with the model. The model is one box in the diagram. The system around it, retrieval, actions, and audit, is the interview.
  • Ignoring the money. A support agent can issue refunds. Skipping permissions and limits on actions is a serious miss.
  • No evaluation story. If you cannot say how you would measure quality, the design is unfinished.
  • Mixing tenants. Enterprise customers demand isolation. Mention it early, before the interviewer asks.

How to Prepare

TAGS
System Design Interview
CONTRIBUTOR
Arslan Ahmad
Arslan Ahmad
ex-FAANG engineering manager and author or Grokking series.
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