Design an enterprise RAG agent system
Company: American
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Technical Screen
Design an enterprise AI assistant for internal company knowledge. The system should answer employee questions over documents such as policies, product manuals, support tickets, reports, PDFs, spreadsheets, and knowledge-base articles. Start with retrieval-augmented generation, but allow agentic behavior when the task requires multi-step reasoning or tool use.
Discuss the following:
- What is the difference between a base language model, a RAG application, and an agent?
- How do agents gain capabilities through tools, APIs, planners, and execution policies?
- How would you design memory for the system, including short-term conversation state and longer-term user or task memory?
- How would you evaluate answer quality, grounding, task success, latency, and cost?
- How would you defend against prompt injection, unsafe tool execution, and data leakage?
- How would you support many concurrent users while keeping sessions isolated and the system reliable?
- How would you build long-running or multi-agent workflows that can pause, retry, recover from failures, and remain durable?
- What document types would you expect in a large enterprise, and what technical challenges do they create for ingestion, indexing, and retrieval?
Quick Answer: This question evaluates a candidate's competency in ML system design for enterprise retrieval-augmented generation and agent architectures, covering understanding of model roles, agent tool integrations, memory and state design, evaluation metrics, security and privacy defenses, concurrency and reliability, workflow durability, and document ingestion/indexing challenges. It is commonly asked in ML system design interviews to assess architectural thinking and trade-off analysis, testing both conceptual understanding and practical application skills for building scalable, secure, and cost-effective enterprise AI assistants.