To find talent capable of managing an AI stack, traditional interviews are insufficient. A better test is to provide candidates with platform credits (e.g., Replit) and challenge them to build a functional agent that automates a real business task, proving their practical skills.

The VP of Search stated that technical interviews must now assess a candidate's ability to use AI coding assistants effectively. The goal is to measure not only problem-solving skills but also fluency with new tools that change how the job is performed, going beyond simply asking un-googleable questions.

Sierra transformed its hiring by replacing traditional coding challenges with real-world tasks. Candidates get a prompt and a $150 token budget to build an application using their preferred AI coding agents. This tests modern, AI-native problem-solving skills, not rote memorization or algorithm theory.

Vercel's hiring has fundamentally changed. Instead of hiring for specific tasks, they look for people who can build and manage agents to perform those tasks. A new key interview question is: "Walk me through how you would create the agent that solves the job that traditionally someone in your position would do."

Dreamer's hiring process now evaluates an engineer's ability to work with and through AI coding agents. Beyond a basic coding screen, the main interview involves a project built using tools like Codex, testing the candidate's skill in prompting, reviewing, and orchestrating AI to be productive.

To cut through the hype, ask candidates to screen share during an interview and walk through their personal AI workflows. This provides an immediate, unfiltered view of their actual proficiency and whether they operate beyond simple chatbot usage.

Since coding agents can perform like junior engineers, the value of simply writing code quickly and correctly is diminishing. The new critical skill for engineers is the ability to judge AI-generated code, architect systems, and effectively steer agents to implement a high-level design.

To assess a candidate's ability to use AI as a thinking partner, have them solve a problem with an LLM. The key is observing their follow-up prompts and their ability to guide the AI step-by-step, rather than just accepting the initial output.

Since AI assistants make it easy for candidates to complete take-home coding exercises, simply evaluating the final product is no longer an effective screening method. The new best practice is to require candidates to build with AI and then explain their thought process, revealing their true engineering and problem-solving skills.