← Early-stage Startup Interview Insights
Frame your answer around the idea that agentic coding is a collaborative workflow where you direct, verify, and iterate with the AI, not just write prompts. Emphasize concrete techniques like task decomposition, context management, and rigorous validation that demonstrate engineering judgment and adaptability in ambiguous startup environments.
Pro tip: Show that you treat the AI as a junior engineer: you set clear acceptance criteria, review its output critically, and know when to intervene manually. This signals maturity and prevents over-reliance on the tool.
Break down the coding task into small, verifiable subtasks that the AI can handle sequentially, reducing ambiguity and enabling incremental progress.
Provide the AI with relevant codebase context, architectural constraints, and clear success criteria to guide its output effectively.
After each AI-generated change, run tests, review diffs, and validate against requirements; iterate with targeted feedback rather than accepting output blindly.
When the AI goes off track, adjust your approach—refine prompts, add examples, or take over manually—demonstrating flexibility and problem-solving.
After the task, evaluate what worked and what didn't, and refine your workflow for future agentic coding sessions, showing a growth mindset.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Emphasize that code review is about verifying intent, behavior, and risk rather than knowing every syntax detail. Describe a systematic process: clarify requirements, run and test the code, use tools and documentation, and focus on logic, security, and maintainability. Show that you can leverage AI as a review assistant while maintaining human judgment.
Pro tip: Treat AI-generated code as you would a junior developer's pull request: you don't need to know every line, but you must understand what it's supposed to do and verify it does that safely. Use AI to explain unfamiliar code, but never trust its explanations blindly—cross-check with official docs and tests.
Before reviewing, ensure you understand what the code is supposed to accomplish, its inputs/outputs, and any constraints. Ask the author (or yourself) for a clear description of the expected behavior.
Execute the code with representative inputs, including edge cases, and observe the results. Write or run unit tests to verify correctness and catch obvious errors.
Leverage linters, static analyzers, and security scanners for the language/framework. Consult official documentation and community resources to understand unfamiliar patterns.
Prioritize reviewing logic, security vulnerabilities, error handling, and performance bottlenecks. Check for common pitfalls like injection flaws, race conditions, or improper resource management.
If uncertainty remains, ask a colleague familiar with the language/framework to review critical sections. Use pair review or async comments to share knowledge.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.