Five Failure Modes Evals Won’t Catch and What to Do About Them

Here's the thing: relying solely on evals can lead you down a rabbit hole of false confidence. They may tell you that your model is performing well, but if they miss critical failure modes, you could be shipping a product that's less effective than you think. The article mentions five specific gaps, but it doesn’t dive into how to actually mitigate these risks. It’s not enough to define what a bad answer looks like; you need a robust framework that ensures those definitions cover edge cases. Otherwise, you're just inviting trouble into your production environment.

What they're not saying is that the context around these evals is just as crucial. If your evals are based on an incomplete understanding of what constitutes failure, you're setting yourself up for a rude awakening post-deployment. This is especially true when you consider the stakes in AI/ML—misleading results can lead to costly decisions. Teams using tools like MLflow or Weights & Biases might find themselves in a similar boat if they don't adapt their eval strategies to these shortcomings.

To be clear, this isn't just about tweaking your eval process; it’s about fundamentally understanding your model's limitations. Who benefits most here? Teams that are serious about deploying AI agents into high-stakes scenarios—think healthcare or finance—where the cost of failure is significant. If you’re in that boat, you need to reevaluate your evals and consider methodologies that go beyond traditional thresholds.

So, what should you do? Don't just adopt evals as a checkbox in your process; scrutinize them. Look into frameworks that can help you catch those failure modes before they become problematic. Your model's performance is only as good as your evals' ability to capture its weaknesses. It’s time to put in the work to ensure your agents can stand up to real-world challenges.