One lesson that's becoming hard to ignore is that model upgrades should be treated more like dependency migrations than simple configuration changes. A model can improve on every benchmark and still break downstream systems because those systems have quietly adapted to its previous behavior.
We've seen similar considerations come up at IT Path Solutions when moving AI applications toward production. Beyond evals, it's worth validating structured outputs, tool-calling behavior, and downstream integrations under real traffic before widening the rollout.
The line that stood out most was that model rollouts fail confidently. That's exactly why shadow traffic, canary releases, and rollback plans are becoming just as important as choosing the next model version.
That's a great way to frame it. Treating model upgrades as dependency migrations captures the problem much better than calling them configuration changes. The biggest production failures I've seen weren't because the model regressed, they were because downstream systems had silently coupled themselves to the previous model's behavior. Shadow traffic and evals reduce the risk, but canary releases and rollback planning are what expose those hidden contracts before they become incidents.
One lesson that's becoming hard to ignore is that model upgrades should be treated more like dependency migrations than simple configuration changes. A model can improve on every benchmark and still break downstream systems because those systems have quietly adapted to its previous behavior.
We've seen similar considerations come up at IT Path Solutions when moving AI applications toward production. Beyond evals, it's worth validating structured outputs, tool-calling behavior, and downstream integrations under real traffic before widening the rollout.
The line that stood out most was that model rollouts fail confidently. That's exactly why shadow traffic, canary releases, and rollback plans are becoming just as important as choosing the next model version.