Model upgrades from AI labs can cause silent regressions in agent performance because improvements in reasoning or coding often alter the specific behavioral nuances required for production stability. Unlike traditional software deployments, these updates lack stack traces or deployment logs within a developer's own git history, often passing standard validation tests that only check for structural integrity, such as JSON parsing. The author identifies three critical technical shifts: changes in response shape (length and formatting), altered tool-calling patterns (increased latency or unexpected tool avoidance), and shifts in how the model handles ambiguity when resolving underspecified requests. To mitigate these risks, the author advocates for "harness engineering" rather than simple prompt engineering. This involves establishing a frozen baseline by running a set of real-world, uncurated requests through the current model and recording human-verified verdicts before an upgrade occurs. By running the same set against the new model version, developers can generate a "diff" to compare outputs. The author emphasizes that while the execution of these tests can be automated, the qualitative judging must remain human-led to determine if a behavioral shift is a functional improvement or a regression. Building this evaluation machinery before an upgrade is live is essential to prevent contaminated baselines.