Governed Capability Evolution: Lifecycle-Time Compatibility Checking and Rollback for AI-Component-Based Systems, with Embodied Agents as Case Study

TL;DR

This paper introduces a staged framework for safe AI capability upgrades in embodied agents, ensuring compatibility and rollback, demonstrated through a robotics testbed with improved safety and success rates.

Contribution

It formulates capability evolution as a software lifecycle problem and proposes a staged validation framework with four compatibility checks for safe upgrades.

Findings

Naive upgrades reach 72.9% success but 60% unsafe activations.

Governed upgrades maintain 67.4% success with zero unsafe activations.

Rollback succeeds in 79.8% of drift scenarios.

Abstract

Software systems built from versioned AI components increasingly need lifecycle-time governance: when a capability module evolves into a new version, the hosting system must decide whetmeher the new version may be activated safely, under what deployment conditions, with what monitoring, and when it should be rolled back. Existing software-deployment patterns (canary, blue-green, feature flags, MLOps pipelines) address parts of this loop but were designed for stateless web services rather than stateful, policy-constrained runtimes that drive AI components in the field. We study this problem in the setting of embodied agents, where capabilities are packaged as installable modules under runtime policy and recovery constraints. We formulate governed capability evolution as a first-class software-lifecycle problem for AI-component-based systems and propose a staged upgrade framework that treats every new capability version as a governed deployment candidate rather than an immediate replacement. The framework introduces four compatibility checks (interface, policy, behavioral, recovery) and organizes them into a staged pipeline of candidate validation, sandbox evaluation, shadow deployment, gated activation, online monitoring, and rollback. A reference prototype on a PyBullet/ROS 2 testbed evaluated over 6 upgrade rounds with 15 random seeds shows naive upgrade reaches 72.9% task success but drives unsafe activation to 60% by the final round, while governed upgrade retains comparable success (67.4%) with zero unsafe activations across all rounds (Wilcoxon p=0.003). Shadow deployment surfaces 40% of regressions invisible to sandbox alone, and rollback succeeds in 79.8% of post-activation drift scenarios. The work extends runtime governance from action execution to capability evolution.