Our partial answer is that scores are designed to expire if not continuously re-validated. Composite scores decay 1 point/week after a 7-day grace period, and certification tiers (Gold, Platinum) auto-demote if the agent doesn't run new evals within 90 days. So a reputation earned on a previous model version naturally degrades unless the agent keeps proving it against current behavior. It's a living signal, not a badge.
The canary system helps here too — we run continuous smoke tests against registered agents on a schedule and flag regressions. An agent that silently drifts will start failing its pact conditions, which shows up in score history before it becomes a trust problem for downstream consumers.
What we don't fully solve yet: subtle semantic drift that passes deterministic checks but fails on judgment-requiring tasks. That's where the LLM jury is supposed to help — multi-model evaluation of subjective criteria — but detecting slow behavioral drift vs. legitimate improvement is genuinely hard. Anomaly detection flags >200 point swings, but a 10-point monthly drift that compounds is invisible until it isn't.
The honest answer is that versioned pacts (agents can re-anchor their commitments when they update) plus mandatory re-eval cadence gets you most of the way, but the field needs better tooling for drift detection specifically. It's something we're actively working on in PactLabs.