System design: design an AI assistant that helps a bank's customer service agents answer customer questions.

Build an agent-assist tool, not an autonomous bot: it reads the conversation and the customer's records (with the agent's own permissions), retrieves the relevant policies and procedures, and drafts an answer with citations that the human agent checks and sends. Actions such as refunds or card blocks stay in the bank's existing systems, behind their normal controls. Wrap it in strong data protection, audit logging, evaluation and monitoring.

1. Clarify requirements (say these out loud)

• Users: internal service agents, on chat and phone.
• Goals: faster, more consistent answers; shorter training for new agents.
• Constraints: strict data protection and regulation, audit trails, data residency, no automated decisions affecting customers without human review.
• Scale: for example 2,000 agents, 50,000 conversations a day.

2. Architecture

1. Inputs: the live chat or call transcript (speech-to-text for calls), and the customer id.
2. Customer context: read-only tools calling the bank's APIs (accounts, recent transactions, open cases) with the agent's identity, returning only the fields needed.
3. Knowledge retrieval: policies, product terms and procedures in a search index (hybrid search and reranking), with versions and effective dates, filtered by the agent's entitlements.
4. Generation: a prompt with the policy passages, customer context and conversation; output as a draft reply plus citations to the policy passages, and suggested next steps (for example "open a dispute in system X").
5. Agent interface: inside the existing service desktop; the agent edits and sends; one-click feedback.
6. Data protection: masking of card numbers and other sensitive fields before the model; a model hosted in the bank's approved cloud and region, with retention terms agreed (zero retention where required).
7. Logging and audit: every draft, its sources, the agent's edits and what was sent.

3. Safety and controls

• No write tools for the model. Actions happen in core systems with existing controls.
• Customer messages are untrusted input (possible prompt injection); kept clearly separated as data.
• Output checks: no account numbers or other customers' data in drafts; tone and compliance phrases.
• High-risk topics (complaints, vulnerable customers, fraud) flagged for special handling.

4. Evaluation and rollout

• Evaluation set from past conversations with approved answers, graded for policy correctness, completeness and tone, with compliance review of samples.
• Shadow mode, then a pilot team, then gradual rollout, measuring handle time, first-contact resolution, quality audits and agent satisfaction.

5. Operations

• Policy updates flow into the index the same day; superseded versions removed.
• Monitoring of latency (drafts within a few seconds), cost, citation accuracy and edit rates.
• Model upgrades through the evaluation gate.

Trade-offs to mention

Agent-assist is slower to show savings than full automation, but much safer in a regulated setting, and it builds the evaluation data needed to automate simple cases later.

What interviewers look for: clarifying requirements, a human in the loop, permission-aware data access, citations, strong data protection, and a measured rollout.