Describe an AI agent or agent harness you built for a production workflow.A strong answer should explain the workflow objective, model and tool choices, instructions or skills, MCP or API integrations, guardrails, evaluation approach, observability, and measurable results. It should also cover how the candidate handled failures, unsafe output, and human review.
How would you design a reliable prior-authorization automation workflow for a clinic?I would model it as a durable, auditable workflow with clear states for data collection, payer verification, submission, follow-up, and exception handling. I would use structured outputs, validate every external request, retain traceable records, add human escalation for uncertainty, and monitor completion time, error rates, and authorization outcomes.
How do you safely work with PHI in application services and AI-enabled features?I apply least-privilege access, encrypt data in transit and at rest, avoid PHI in logs and prompts where possible, and maintain auditable access and change records. I also use data minimization, redaction, controlled retention, and explicit review of third-party model and vendor data-handling terms.
Tell us about a feature you owned from an unclear problem to production.A strong response should show how the candidate gathered context from users, defined success metrics, wrote a lightweight specification, made pragmatic technical decisions, shipped an initial version, and iterated using production feedback. It should demonstrate ownership of deployment, monitoring, and post-launch improvements.
How do you use AI coding agents while maintaining code quality in a large codebase?I provide repository-specific instructions, scoped tasks, relevant tests, and clear acceptance criteria. I run independent tasks in parallel when appropriate, review generated diffs critically, validate architecture and security implications, execute tests, and treat agent output as a draft rather than an unverified final implementation.
