Intelligence as Managed Autonomy: Failure, Escalation, and Governance for Agentic AI Systems
arXiv:2605.27628v1 Announce Type: new 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,
The rapid deployment and increasing autonomy of AI systems are highlighting critical challenges related to their control, reliability, and governance, making theoretical frameworks like 'managed autonomy' increasingly relevant.
A strategic reader should care because this paper introduces a foundational concept for designing and regulating advanced AI, directly addressing the core risks of autonomous agents and proposing a path toward responsible deployment.
The focus is shifting from solely fault-finding in model limitations to architectural vulnerabilities in unbounded autonomy, proposing a new design philosophy that incorporates failure detection and escalation mechanisms within AI systems.
- · AI governance frameworks
- · Robotics and autonomous systems developers
- · Cybersecurity firms
- · Regulators
- · Unregulated AI deployments
- · Systems with unbounded autonomy
- · Developers ignoring safety protocols
This theoretical framework will influence the development of safer and more robust AI agents capable of self-regulation and error management.
It will drive the creation of new standards and regulatory requirements for AI systems, particularly in high-stakes environments, by emphasizing detection of epistemic drift.
The concept of 'managed autonomy' could enable greater societal trust and adoption of sophisticated AI, paving the way for more complex human-AI collaboration by mitigating risks upfront.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI