Agentic AI interview question · Agent system design · senior level
Design a customer support agent for an e-commerce company.
Free to read in full. This is 1 of the 120 questions in the Agentic AI Engineering interview pack, with 153 diagrams. Get the pack, ₹999 · $20
Why it is asked
A classic agent design loop: the interviewer checks scoping, tool permissions with limits, escalation, and how you measure resolution without hurting customers.
The answer
I would build an agent that resolves the top few ticket types end to end (order status, returns, address changes, small refunds) and hands everything else to humans with a clean summary.
Requirements: chat and email; fast chat replies; verified identity before account actions; refunds only within policy; full audit. Scale assumption: tens of thousands of tickets a day.
Architecture:
- Channels: web chat and the existing helpdesk software (for example, Zendesk) for email.
- Agent: a router sends each ticket to a skill (order status, returns, refunds), each with a narrow prompt and only its own tools.
- Tools: order lookup (read), return label creation, refund through the payments API with a hard limit in code, say $50 without approval. The model never sees card data.
- Knowledge: retrieval over policy documents, with the policy version cited in the answer.
- Guardrails: identity check before any account tool, refund limits per order and per customer per month, and output checks for promises the policy does not allow.
- Escalation: on request, over limit, low confidence, or when the customer is angry, with a structured summary.
Trade-offs: higher refund limits raise automation but also fraud risk; narrow skills are easier to test than one general agent.
Evaluation and rollout: replay a few thousand past tickets with known outcomes; track resolution rate, wrong-action rate, escalation rate and satisfaction. Ship a skill only when replayed tickets show a wrong-action rate under 1% and a resolution rate (no reopen) at or above the human baseline. Start with order status, then returns, then refunds behind approval.
What the interviewer listens for
- Scope to top ticket types, hand off the rest
- Router to narrow skills with their own tools
- Refund limits in code, identity check before account actions
- Policy retrieval with versioned citations
- Replay past tickets; staged rollout by ticket type
Follow-ups the interviewer may ask
- How do you stop refund fraud through the agent? Hint: per-customer limits, velocity checks, and the same fraud signals the human team uses.
- What is your north-star metric? Hint: resolved without human and without reopen within 7 days, next to satisfaction.
Common mistakes
- One general agent with every tool
- Refund limits written only in the prompt
- Measuring deflection (tickets kept away from humans) instead of real resolution
Written by Roni Das, who builds distributed systems and AI systems for a living.
Learn it properly
This is taught step by step in the course lesson LLM Agents in Production: The Model in a Loop.
One question is not enough for the interview.
This is 1 of the 120 questions in the Agentic AI Engineering pack
Each question has why it is asked, a full answer, what the interviewer listens for, the follow-ups, the common mistakes and a diagram (153 diagrams in all). A PDF you keep, junior to staff level.
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