A new paper lays out a formal checklist for making sure an AI agent still works after you swap its underlying model, move it to a new region, or scale it up.
The authors call it "agent calibration": a process that checks an agent against three standards - basic capability, technical environment, and user context - and flags where it falls short. Fixes can mean swapping tools, adding examples and task descriptions, or training a smaller model to handle the gap, all rechecked against the same standards under a fixed budget. Qualification is strict: every mandatory test, hard rule, and deployment path has to pass, so a pile of average-case wins can't cancel out one hard failure. The paper is explicit that this is a proposed framework rather than a working tool - the authors list an automated version as future work and say empirical validation is still pending.
The underlying problem is real even if the fix is unproven: the paper notes that swapping in a new model doesn't make an agent uniformly better, since it can turn a previously correct answer into an error and an error into a correct answer in the same breath. Most teams currently handle model swaps with spot checks and gut feel rather than a structured audit, and the stakes rise further once "new jurisdiction" also means new compliance rules, not just a different server region.
Software engineering solved a version of this problem decades ago with regression test suites; AI agents are still catching up, and this paper is a blueprint for that catch-up, not a shipped one.