Toward Responsible Autonomy: A Governance Framework for Large-Scale Agentic AI Systems

Autonomous AI agents—systems that invoke external tools, maintain goal-directed behavior across multiple sequential steps, and write to persistent state—introduce safety failures that output-level evaluation frameworks cannot detect. We formally show that any monitoring policy conditioning only on the current action cannot reliably detect failure modes whose definitions require access to the prior execution state—specifically Goal Drift and Hallucination Cascades. We introduce the Responsible Agentic Lifecycle (RAL), a five-component modular governance framework that unifies goal alignment monitoring, hallucination cascade detection, and resource constraint enforcement into a single trajectory-conditioned Safety Score S(t) computed over execution history ℋₜ. The Goal Anchoring Protocol (GAP, Algorithm 1) provides a runtime pseudocode specification for semantic goal drift detection and re-anchoring. S(t) is bounded, monotone in each constraint cost, and trajectory-sensitive by construction.