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Design an AI customer-support agent

  • System design
  • Asked in most interview loops
  • About 2 h 30 min to learn
  • 3 vetted resources
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What this topic covers

A support agent answers customers from the help centre and policy documents and takes real actions on their account, like refunds or address changes, through permissioned APIs. Interviewers like it because it touches every hard part at once: grounding, tool safety, when to hand off to a human, and cost. A strong answer separates what the model may do alone from what needs a policy check or a person, routes easy questions to a cheaper model, and passes the full context on handoff. It measures success by resolution rate and harmful actions, not by how fluent replies sound, and it tests with simulated users and pass^k so you know the agent behaves the same way across repeated tries.

Builds on

You’re ready when you can

What interviewers check, from the resources below.

  • Can break a support conversation into separate tasks you can test one by one
  • Can name task-level and business metrics, including escalation accuracy and resolution or deflection rate
  • Can explain where retrieval and tool calls sit in the request flow
  • Can describe when and how the agent hands a conversation to a human
  • Can explain why an LLM-simulated customer gives a more realistic test than a fixed script
  • Can define pass^k and why it drops as k grows
  • Can explain why grading on final database state is more objective than judging the transcript
  • Can name policy-following as a common failure mode
  • Can sketch an eval harness with a user simulator, tools, policy, and a goal-state check
  • Can compute pass^k from repeated trial results

What to study, in order

Each link opened and checked by hand; the date says when.

  1. Start here

    Customer support agent (opens in a new tab)

    Claude Docs (Anthropic) · 45 min · Free · checked 21 Oct 2026

    Why
    Most interviewers open this case by asking how you’d define success and which tasks the agent should own. This guide walks through exactly that before any code, which gives you a clean structure to talk through.
    What to read
    Read “Before building with Claude” in full, especially “Break the interaction into unique tasks” and “Establish success criteria” (note escalation accuracy and deflection rate). Then read “Add dynamic and agentic capabilities with tool use”, “Evaluate your prompts”, and “Integrate Claude into your support workflow”.
    What to skip
    Skip “Prerequisites”, “Build your user interface”, and the long example prompt text about car insurance; the Streamlit code isn’t what gets discussed in an interview.

  2. Free alternative

    𝜏-Bench: Benchmarking AI agents for the real-world (opens in a new tab)

    Sierra · Karthik Narasimhan · 20 min · Free · checked 21 Oct 2026

    Why
    Saying “I’d test with simulated users and report pass^k” is what separates a strong support-agent answer from a demo. This short post from the team behind 𝜏-bench explains why.
    What to read
    Read “Background”, “TAU (𝜏)-Bench: A tool-agent-user benchmark”, and “Key findings”. Focus on the simulated user, the policy documents the agent must follow, grading by final database state, and how scores fall as k rises.
    What to skip
    Skim “Towards more reliable agents in the real-world”; it’s mostly a forward-looking wrap-up.

  3. Go deeper

    τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains (opens in a new tab)

    arXiv · Shunyu Yao, Noah Shinn, Pedram Razavi, Karthik Narasimhan · 60 min · Free · checked 21 Oct 2026

    Why
    If an interviewer pushes on how you’d build the eval harness for your agent, the paper gives you the concrete setup: domain APIs, policy documents, a simulated user, and goal-state checks.
    What to read
    Read the abstract and introduction, then the sections describing the benchmark design and the pass^k metric, and the results and failure analysis for the retail and airline domains.
    What to skip
    Skip the detailed per-model result tables and the appendix of task examples unless you want to build a similar benchmark.

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