How You Use AI Tools at Work and When You Would Delegate a Task to an Agent

During a technical screen for a software engineering role, the interviewer asks how you use AI tools in your everyday engineering work, then pushes further on whether you would hand a task to an autonomous agent that selects its own tools. Answer both parts with concrete examples from your real work, in about a minute each. ### Constraints and Clarifications - Use your own experience. If you use AI tools only lightly, say so and explain your reasoning rather than overstating your usage. - The interviewer is interested in specifics: which tasks, where the limits are, and how you check the output. ### Clarifying Questions - By "agent," does the interviewer mean a tool that edits code and runs commands in your environment, or a chat assistant that only proposes changes? - Is the question about your personal workflow, or about how a team should adopt these tools? ### Part 1 — How You Use AI Today How do you use AI tools in your day-to-day work? Name the specific tasks where they help, the tasks where you avoid them, and how you verify what they produce. ```hint Anchor on one real task Pick one recent piece of work and walk through where the tool saved time, where it was wrong, and what caught the mistake. ``` #### What This Part Should Cover - Concrete use cases tied to real engineering work rather than a list of tool names. - Explicit boundaries: tasks you keep for yourself and why. - A verification habit, such as tests, review, or reproduction, that fits the risk of each task. ### Part 2 — Delegating to an Agent That Chooses Its Own Tools Would you currently delegate a task to an agent that picks its own tools and acts on its own? Which kinds of tasks, under what safeguards, and which would you not delegate? ```hint Think in terms of blast radius Consider what the agent can touch, how easily its actions can be reversed, and how you would know whether its result is correct. ``` #### What This Part Should Cover - Criteria for deciding whether a task is suitable to delegate, such as reversibility, access to production or secrets, and whether correctness is checkable. - Safeguards like scoped permissions, sandboxed environments, required review, and tests that define "done." - An honest position, with reasoning, rather than blanket enthusiasm or blanket refusal. ### What a Strong Answer Covers - Two concise, specific answers that sound practiced but not memorized, each with a real example. - A consistent principle linking the two parts: the level of autonomy matches how verifiable and reversible the work is. - Awareness of risks such as confidently wrong code, leaked credentials or proprietary data, and unreviewed changes, plus concrete mitigations. - Accountability: the engineer remains responsible for what ships, regardless of who or what wrote it. ### Follow-up Questions 1. Tell me about a time an AI tool gave you a plausible but wrong answer. How did you notice? 2. How would you review a pull request that was mostly written by an agent? 3. What team-level rules would you propose for using these tools with confidential code?