Author(s): Shahidullah Kawsar

Originally published on Towards AI.

AI Engineer Interview Preparation

1. A large subscription platform builds an AI assistant that can check account status, update billing details, cancel subscriptions, and revoke admin access. A product manager suggests allowing the model to choose every next step as long as the user sounds confident. In a structured production workflow, what should the application control instead of leaving it fully to the model?

The article presents a series of MCQs about designing production workflows for AI agents, emphasizing that applications—not the model—should own safety controls, state machines, permissions, approvals, and tool policies. It explains how to classify tool risk based on side effects and impact (including read-only data exposure), how to guard against destructive operations with impact estimation and human approvals, and how to handle retry safety using idempotency keys. It also discusses human-in-the-loop patterns like batch approval for multi-step onboarding to reduce confirmation fatigue, escalation policies based on risk and execution context (e.g., refund amounts), and the need to strengthen controls when many accounts or privileged access are affected. Finally, it covers testing approaches using mock tools first, and logging designs that preserve auditability while redacting sensitive fields to prevent logs from becoming a second data leak.

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Published via Towards AI