Intelligence as Managed Autonomy: Failure, Escalation, and Governance for Agentic AI Systems
Srini Ramaswamy
Why It Matters
What makes this one worth your time
Understanding and managing AI system failures is crucial for developing reliable and safe autonomous systems, especially in critical domains like healthcare and robotics.
The paper introduces a framework for managing AI autonomy and failures using a structured state model.
Summary
The paper proposes a theory of managed autonomy for AI systems, focusing on detecting epistemic drift and managing failures through a structured framework called SMARt, which includes Stable, Meta-cognitive, Assisted, and Regulated states. It uses a timed, guarded Petri net formulation to ensure system reliability and governance, and discusses the application of domain-specific triggers to maintain safety across different environments.
Key contributions
- Introduction of the SMARt model for managing AI autonomy.
- Development of a timed, guarded Petri net formulation for system reliability.
- Proposal of domain-specific trigger sets to maintain safety across varied operational settings.
Notable insights
- The concept of managed autonomy that includes the ability to detect epistemic drift and suspend reasoning is a novel approach to AI reliability.
- Using a timed, guarded Petri net formulation to establish bounded properties for AI systems is an innovative method for ensuring governance and safety.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2605.27628v2 Announce Type: replace Abstract: As autonomous and agentic AI systems scale in robotic and human-machine environments, managing hallucination and persistent but unjustified action remains an open challenge. Rather than attributing these failures solely to model or alignment limitations, this paper explores the architectural vulnerability of unbounded autonomy - the presumption that an agent should continue operating regardless of rising uncertainty. It introduces a theory of managed autonomy that defines intelligent behavior through the formal capacity to detect epistemic drift, suspend reasoning, attempt recovery, and ultimately surrender control when reliability diminishes. We instantiate this theory via the SMARt (Self-Managing Multi-tier Autonomous Reasoning with Regulated/Revoked transitions) model, a four-layer framework featuring Stable, Meta-cognitive, Assisted, and Regulated states. By developing a timed, guarded Petri net formulation, we establish theoretically bounded properties for the system, demonstrating how architecture can formally mandate escalation, constrain invalid outputs, and ensure governance reachability under specified conditions. We further analyze how incorporating domain-specific trigger sets across varied operational settings (e.g., healthcare, robotics, etc.) can systematically preserve safety, assuming completeness and soundness criteria are met. Because these triggers are designed to be adaptive, the SMARt model accommodates the safe, controlled expansion of an agent's operational scope over time. We conclude that formalizing failure management within the autonomy lifecycle is a crucial step toward realizing reliable and governed artificial intelligence.