Guardrails & Evals: Keeping your AI skills honest

You built a Skill. It worked on day one. You demoed it, people nodded, you moved on to the next thing. 

Then someone edited the prompt. Or the model version bumped underneath you. The output got a little worse, and nothing broke - no exception, no red build, nothing in the logs. You found out two weeks later when a colleague forwarded you something embarrassing. 

That's drift, and most of us have exactly zero instrumentation for it. 

A guardrail stops your Skill doing dumb things while it's running. An eval tells you afterwards whether it's still doing its job. Put together, they're the regression suite your AI tooling doesn't have yet, and honestly you already know how to build one, because you've been writing test suites your entire career and nobody mentioned that the same discipline applies over here. 

What you'll walk out with:

• A clear line between a guardrail and an eval, and which one your problem needs
• Three tiers of checking, and why running the expensive one on everything is how budgets die
• Real token costs, including the run that got away
• One guardrail and one eval added to a Skill you already own, before the week's out