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December 2026
Journal Article
Title
Governed capability evolution: Lifecycle-time compatibility checking and rollback for AI-component-based systems, with a proof-of-concept evaluation on embodied agents
Abstract
Context:
Software systems built from versioned AI components need lifecycle-time governance over how a new module version is admitted, monitored, and withdrawn. Established deployment patterns (canary, blue–green, feature flags, MLOps) monitor aggregate service-level signals of stateless services, not the stateful, policy-constrained runtimes that drive AI components.
Objectives:
We formulate governed capability evolution as a software-lifecycle problem, and ask whether staged upgrade governance prevents unsafe activations where practice-adapted canary and blue–green deployment do not, and at what cost.
Methods:
We propose a staged upgrade framework with four compatibility checks (interface, policy, behavioral, recovery) and a seven-stage pipeline (validation, sandbox, shadow, gated activation, monitoring, rollback, audit). A reference prototype is evaluated in a behavioral simulation of an embodied manipulation stack, with a PyBullet backend replicating the strategy ordering. Upgrade candidates come from a seeded fault-operator generator (45 per capability family and seed, including out-of-taxonomy probes) rather than hand-picking, and thresholds are fixed on a disjoint development set. We compare five strategies over five upgrade rounds and 15 seeds with paired Wilcoxon tests under Holm–Bonferroni correction.
Results:
Naive upgrade reaches 73.3% final-round success but activates unsafe versions in 74.7% of rounds. Canary and blue–green adaptations keep comparable success yet still admit faulty versions in 61.3% and 64.0% of upgrade rounds, because aggregate success hides policy and recovery regressions. Governed upgrade admits no in-taxonomy fault and one out-of-taxonomy probe in 75 behavioral-track rounds, at a 4.6-point success cost (all contrasts against the three baselines: adjusted 𝑝≤0.024); the full pre-activation stack detects 97.3%–100% of in-taxonomy faults across deployment profiles, and rollback restores verified operation in 72.4% of drift scenarios.
Conclusion:
Bounded-window aggregate-success gating proved insufficient against these fault operators; staged governance closes the gap at a modest, tunable cost on existing CI/CD infrastructure.
Software systems built from versioned AI components need lifecycle-time governance over how a new module version is admitted, monitored, and withdrawn. Established deployment patterns (canary, blue–green, feature flags, MLOps) monitor aggregate service-level signals of stateless services, not the stateful, policy-constrained runtimes that drive AI components.
Objectives:
We formulate governed capability evolution as a software-lifecycle problem, and ask whether staged upgrade governance prevents unsafe activations where practice-adapted canary and blue–green deployment do not, and at what cost.
Methods:
We propose a staged upgrade framework with four compatibility checks (interface, policy, behavioral, recovery) and a seven-stage pipeline (validation, sandbox, shadow, gated activation, monitoring, rollback, audit). A reference prototype is evaluated in a behavioral simulation of an embodied manipulation stack, with a PyBullet backend replicating the strategy ordering. Upgrade candidates come from a seeded fault-operator generator (45 per capability family and seed, including out-of-taxonomy probes) rather than hand-picking, and thresholds are fixed on a disjoint development set. We compare five strategies over five upgrade rounds and 15 seeds with paired Wilcoxon tests under Holm–Bonferroni correction.
Results:
Naive upgrade reaches 73.3% final-round success but activates unsafe versions in 74.7% of rounds. Canary and blue–green adaptations keep comparable success yet still admit faulty versions in 61.3% and 64.0% of upgrade rounds, because aggregate success hides policy and recovery regressions. Governed upgrade admits no in-taxonomy fault and one out-of-taxonomy probe in 75 behavioral-track rounds, at a 4.6-point success cost (all contrasts against the three baselines: adjusted 𝑝≤0.024); the full pre-activation stack detects 97.3%–100% of in-taxonomy faults across deployment profiles, and rollback restores verified operation in 72.4% of drift scenarios.
Conclusion:
Bounded-window aggregate-success gating proved insufficient against these fault operators; staged governance closes the gap at a modest, tunable cost on existing CI/CD infrastructure.
Author(s)