The work changed
Modern engineering interviews often still assume the candidate is writing every line by hand in an isolated editor. That is no longer how many strong engineers work. They use coding agents, shells, logs, package managers, deploy targets, and verification loops to move from an ambiguous request to a working system.
The useful signal is not whether a candidate can avoid AI. It is whether they can direct it, inspect it, constrain it, and recover when it is wrong.
What should be evaluated
Agent-native assessment should capture the full work session: how the engineer reads the task, explores the system, asks for help, tests claims, debugs failures, documents the result, and decides when the work is good enough to hand off.
That means reviewing terminal activity, AI prompts and responses, file changes, runtime state, tests, docs, and the final deployed behavior together. Final code matters, but the path to that code often reveals more about engineering judgment.
A better assessment primitive
BrushPass starts from a simple premise: give each candidate a disposable Linux workspace, route coding-agent traffic through controlled proxy tokens, and record the evidence needed for a reviewer to understand the work.
The result is closer to a real engineering session than a coding puzzle. It tests practical server fluency, debugging, deployment, AI judgment, and communication without giving candidates access to production systems or provider keys.