Cracking AI-Based SWE Interviews

Recently, a candidate asked me:

“How do I prepare for coding interviews where the editor itself is powered by AI? What’s different, and what do interviewers care about?”

This is a timely question. As Agentic AI tools become standard in technical interviews, the expectations and evaluation criteria are shifting. Here’s my advice—tailored for the Agentic AI era—on how to excel in these new-style SWE interviews.

1. Understand the Interview Format

• Agentic AI coding rounds use editors like VS Code, Cursor, or custom platforms with built-in AI agents.
• You’ll get real-time code suggestions, error detection, and sometimes even hints or documentation from the AI.

2. What Interviewers Rate Upon

• Technical fundamentals: Your grasp of algorithms, data structures, and problem-solving remains crucial.
• AI collaboration: Interviewers watch how you interact with the AI agent. Do you use its suggestions wisely? Can you spot and correct its mistakes?
• Critical thinking: Are you evaluating AI-generated code, or just accepting it blindly?
• Communication: Can you explain your reasoning, especially when you accept or reject AI help?
• Adaptability: How smoothly do you switch between manual coding and leveraging AI features?

3. How These Interviews Differ from Traditional Rounds

• AI is your coding partner: Instead of a static editor, you’re working with a tool that can suggest, complete, and even debug code.
• Process over product: Interviewers care about how you solve problems, not just the final answer.
• Real-time feedback: Expect questions about your choices—why you used (or ignored) an AI suggestion, how you debugged, etc.
• Less focus on memorization: The ability to use tools effectively is valued over rote recall.

4. Tips to Excel in Agentic AI Coding Interviews

• Practice with AI-powered editors: Get comfortable with Copilot, Cursor, or similar tools. Learn their strengths and limitations.
• Be skeptical, not cynical: Review every AI suggestion. Accept what’s correct, modify what’s close, and reject what’s wrong.
• Narrate your process: Explain your approach out loud, especially your interactions with the AI.
• Use AI for speed, not shortcuts: Let the agent handle boilerplate, but do the core logic yourself.
• Test thoroughly: Use the editor’s instant feedback to run edge cases and validate your solution.
• Stay calm under feedback: If the interviewer challenges your choices, respond thoughtfully and show your reasoning.

5. Common Pitfalls in Agentic AI Coding Interviews

Mistakes candidates often make:

• Over-reliance on AI: Accepting AI-generated code without understanding or verifying it.
• Ignoring AI errors: Failing to spot hallucinations, security risks, or incorrect logic in AI suggestions.
• Poor communication: Not explaining their reasoning, choices, or why they accepted/rejected AI help.
• Lack of testing: Relying on walkthroughs instead of thorough in-code verification and edge case testing.
• Not asking clarifying questions: Jumping into coding without fully understanding the problem or constraints.

How to avoid these pitfalls:

• Use AI as a tool, not a crutch—always review and test its output.
• Communicate your thought process, especially when using or modifying AI suggestions.
• Ask clarifying questions before starting, and narrate your approach as you go.
• Test your code thoroughly, including edge cases.
• If the AI makes a mistake, point it out and explain your correction to the interviewer.

6. Real-World Examples: Good vs. Bad Approaches

Example 1: “Implement a function to merge two sorted linked lists.”

• Bad Approach:
  • Accepts AI-generated code without reading it.
  • Doesn’t test for edge cases (e.g., one list is empty).
  • Fails to explain why the AI’s approach works or doesn’t.
• Good Approach:
  • Uses AI to generate a function stub, then manually implements the merge logic.
  • Tests with multiple cases (both lists empty, one empty, both non-empty).
  • Explains the merging process and why each step is necessary.

Example 2: “Find the longest substring without repeating characters.”

• Bad Approach:
  • Asks AI for a solution, copy-pastes it, and runs without understanding.
  • Misses off-by-one errors or fails to handle Unicode/edge cases.
• Good Approach:
  • Discusses possible algorithms (sliding window, hash set).
  • Uses AI for boilerplate, but writes and explains the core logic.
  • Tests with strings like “abcabcbb”, “bbbbb”, and “pwwkew”.
  • Explains why the chosen approach is optimal.

7. Sample Scenario: Agentic AI in Action

Prompt: “Find the longest palindrome in a string. You have access to an AI agent in the code editor.”

Strong approach:

• Use AI to generate a function stub.
• Discuss possible algorithms (expand around center, dynamic programming).
• Ask the AI for documentation or hints if needed.
• Review and test AI-generated code.
• Explain your reasoning and choices to the interviewer.

8. Final Thoughts

Agentic AI coding interviews are not just about writing code—they’re about collaborating with intelligent tools. The best candidates:

• Use AI to enhance productivity, not replace their own thinking.
• Communicate clearly and justify their decisions.
• Demonstrate adaptability and critical thinking.

Embrace the new format, practice with AI tools, and focus on your problem-solving fundamentals.

If you have more questions or want to discuss your interview prep, feel free to reach out!