How to Choose an AI Engineer to Build Agents With Human Fallbacks

Short answer: choose a production software engineer who understands LLMs, not someone who is mainly good at prompts. The hard part of an agent with human fallbacks isn't the clever part. It is deciding when the agent should stop, how a person picks up the case with full context, and how you'll know what the agent did. Test candidates on the failure and escalation path, not on a demo.

I build these agents for a living, so treat this as a scorecard you can also use on me.

The hiring scorecard

If a candidate is strong on prompts but weak on the first two rows, you'll get an impressive demo and a fragile system.

What "human fallback" should mean in their answer

A good candidate describes fallback as part of the design, not an error message. Listen for:

• When to escalate: low confidence, out-of-scope requests, irreversible actions (refunds, deletions, customer emails), repeated tool failures.
• How to escalate: the case lands in a queue your team already uses (helpdesk, Slack, CRM) with the conversation, the agent's reasoning, and what it tried.
• What happens next: the human's decision is logged and, where useful, fed back into the eval set.
• Suggest mode first: the agent proposes, a person acts, and autonomy is widened as the logs earn trust.

Questions to ask in the interview

1. "For our use case, when should the agent stop and hand off? How does the person receiving it get context?"
2. "What is the agent not allowed to do, and how is that enforced?"
3. "How will you test it before launch, and after every prompt or model change?"
4. "If a customer complains about something the agent did last Tuesday, how do we find out exactly what happened?"
5. "What happens if a tool call fails halfway through a multi-step task?"
6. "What will this cost to run per month at our volume?"

Strong answers are specific and slightly boring. Vague answers about how smart the model is are a warning.

Red flags

• Only shows happy-path demos
• Gives the agent broad admin credentials "to keep it simple"
• No plan for evals, logging, or cost limits
• Treats human review as something to "add later"
• Can't explain the difference between an agent and a chatbot in operational terms

A paid trial beats a long interview

Ask the shortlisted engineer for a small, paid piece of work: design the escalation path and eval set for one real workflow. You'll learn more from that document than from any portfolio.

For what production agents need beyond a chatbot, see AI agents vs chatbots vs automations and the AI agent security checklist.

Looking for this kind of engineer?

I'm Rajesh Dhiman, an AI systems engineer based in India. I build custom AI agents with human fallback, evals, and observability for SaaS and support teams worldwide. See custom AI agent development.