Microsoft·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Microsoft SWE interview where they handed me a zip file with a small project and had me use an AI coding agent to find bugs and then add a feature for persisting in-memory data to disk. The twist was having to narrate and explain what the AI was doing as it went, which felt a bit like pair programming with a robot while someone watched over your shoulder.

Questions Asked (1)

You're given a zip file with a small project. Using an AI coding agent of your choice, identify bugs in the codebase and then implement a feature that saves in-memory data to disk. Walk the interviewer through what the AI is doing at each step.

Technical Trade-offsAPI & IntegrationsSystem Design

Author's notes

This was a weird format.

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AI HintsAI Generated

Suggested Approach

Start by briefly outlining your plan: first, use the AI agent to scan and identify bugs, then implement the save-to-disk feature, all while explaining the agent's actions and your reasoning. Emphasize how you guide the AI, validate its output, and make trade-offs. Keep the walkthrough structured and interactive.

Pro tip: Treat the AI as a junior engineer: review its suggestions critically, ask it to explain its reasoning, and be ready to override or refine its output. This shows you're in control and can leverage AI effectively without blindly trusting it.

1. Set up and orient the AI

Explain how you'd load the project into the AI agent, give it context about the codebase and the task, and ask it to summarize the project structure and potential problem areas.

2. Identify bugs with AI assistance

Describe prompting the AI to find bugs, then how you'd verify each finding by reading the code, running tests, or asking the AI to explain its reasoning. Prioritize bugs based on severity and impact.

3. Design the save-to-disk feature

Discuss how you'd use the AI to propose a design, considering trade-offs like file format (JSON, binary), atomic writes, error handling, and performance. Validate the design against requirements.

4. Implement and test the feature

Walk through how you'd have the AI generate code, then review, refactor, and test it. Mention writing unit tests, handling edge cases, and ensuring the feature integrates well.

5. Summarize and reflect

Conclude by summarizing what the AI did, what you learned, and how you ensured quality. Highlight any trade-offs made and potential improvements.

Key Points to Mention

Prompt engineering: how to give clear, specific instructions to the AI agent
Verification: always review AI-generated code and test it thoroughly
Trade-offs: file format, synchronous vs asynchronous saving, error handling strategies
Incremental development: break the task into small steps and validate each
Communication: explain the AI's actions and your decisions clearly to the interviewer
Limitations: acknowledge where AI might fail and how you'd compensate

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.