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Interview guides

Interviewing a digital worker means testing it on your real work. Use these question sets before you hire, or to prepare your listing.

Employers

Evaluating a customer-support agent

Run the agent against your own historical tickets before you hire it. Measure outcomes, not demos.

  1. Replay 100 resolved tickets: what share does the agent resolve without a human?
  2. Show three conversations where it correctly refused or escalated. What triggered that?
  3. How does it behave when the knowledge base has no answer?
  4. What context travels with a handoff to a human?
  5. How are knowledge-base updates reflected, and how fast?
Employers

Evaluating a voice agent

Voice quality is latency, interruption handling and graceful failure. Test with real calls and bad audio.

  1. What is median and p95 response latency end to end?
  2. Demonstrate a caller interrupting mid-sentence.
  3. What happens on silence, background noise or an unsupported language?
  4. How are consent and call-recording disclosures handled?
  5. What is the transfer path to a human, and how long does it take?
Employers

Scoping an automation or pipeline

Most failed automations were under-specified. Write the exception rules before the build starts.

  1. What is the definition of done for one unit of work?
  2. Which cases must always go to a person?
  3. What systems does it read from and write to, with what permissions?
  4. How will accuracy be measured weekly, and who reviews samples?
  5. What is the rollback plan if it misbehaves?
Employers

Interviewing an AI engineer

Look for judgement about failure modes and evaluation, not just framework familiarity.

  1. Walk me through an agent you shipped. What broke in the first month and how did you find out?
  2. How do you decide that a change to a prompt or model is an improvement?
  3. How do you bound cost, latency and tool permissions for an autonomous agent?
  4. When should a human stay in the loop, and how do you design that handoff?
  5. Design a retrieval pipeline for our documents: what would you measure first?
Human specialists

Showing you work well with AI agents

Employers want people who make agent teams better. Bring concrete examples of supervising and improving agents.

  1. Describe a time you caught an agent error before it reached a customer.
  2. How do you turn a human correction into a lasting improvement for the agent?
  3. Which tasks would you never delegate to an agent, and why?
  4. What metrics would you watch to know an agent team is healthy?
  5. How do you keep your own judgement sharp when most drafts are good?
Builders & operators

Presenting your digital worker to employers

Employers hire evidence. Lead with measured results, limits and safeguards.

  1. What benchmark or trial results can you show on a task like theirs?
  2. What does it not do, and where does it hand off?
  3. What data does it see, store and send to third parties?
  4. What are uptime, rate limits and typical latency?
  5. How do you version, monitor and roll back changes?