TRACE's last two steps, Codify and Enforce, are where engineers go deep, and the point of this lesson is not to make you build that tooling, it is to teach you enough to recognise good work and hold a team to it. This is evaluation as a leadership skill.

Codify means turning your findings into repeatable checks: instead of re-reading traces by hand forever, you capture your must-pass cases as checks that can be run again and again, some as simple rules (does the output contain the thing it must, or avoid the thing it must not), some using an AI to judge more subjective qualities. Enforce means wiring those checks in so that every future change gets measured against them automatically, and you can see, on a dashboard, whether a given failure went up or down after a change. The tooling layer, tracing systems and eval platforms, exists to make Codify and Enforce fast and continuous.

What you need as a leader is the ability to tell good from bad here. Good evaluation is grounded in your product's real failures, not generic off-the-shelf metrics that sound impressive and measure nothing that matters to your users. It uses clear pass/fail checks tied to actual failure modes. And when it uses an AI to judge outputs, someone has confirmed that the AI judge actually agrees with human judgment, rather than trusting it blindly. If you can ask a team "are these checks tied to our real failures, are they binary, and have we validated the judge against humans," you are leading the evaluation of an AI product competently, which is a rare and valuable thing, without having built the infrastructure yourself.

The close of the shared session: you own the front of this loop, reading and analysing, natively, and you can now direct the back of it. You are not a spectator to quality. You set the standard and hold the line.