Intent-Driven Execution Boundary Detection: Determining When Autonomous Internet Systems Should Stop, Continue, or Escalate Tasks

The development of autonomous Internet systems and intelligent agents has increasingly shifted computational interaction from manually operated tools toward systems capable of interpreting user intentions, discovering digital capabilities, coordinating heterogeneous resources, and executing multi-stage tasks. However, autonomy introduces an important but relatively distinct research problem: determining when an autonomous system should stop executing, continue pursuing an objective, or escalate a task for additional information, authorization, or human intervention. Although autonomous systems are commonly designed around task initiation, planning, execution, and outcome generation, the determination of appropriate execution boundaries remains essential for ensuring that computational activities remain aligned with user intentions. This paper proposes Intent-Driven Execution Boundary Detection (IDEBD), a conceptual framework for determining whether autonomous Internet systems should terminate, continue, or escalate their execution processes. Rather than treating task execution as an activity that should continue until all potentially relevant operations have been exhausted, IDEBD treats execution as a bounded decision-making process governed by intended outcomes, task scope, outcome sufficiency, expected marginal utility, computational costs, uncertainty, and operational risks. To operationalize this perspective, the paper introduces a three-state execution boundary decision model comprising Stop, Continue, and Escalate (SCE). The framework further introduces six interconnected mechanisms: intent-conditioned execution boundaries, sufficient outcome detection, task scope boundary monitoring, diminishing returns detection, risk-aware escalation assessment, and execution termination verification. These mechanisms collectively support decisions concerning whether an autonomous system has achieved a sufficiently satisfactory outcome, whether additional execution is likely to provide meaningful benefits, and whether unresolved uncertainty or operational constraints require escalation. A central distinction of IDEBD is that it separates the completion of individual operations from the sufficiency of the overall user-requested outcome. An autonomous system may successfully complete a search, retrieve additional information, or execute a tool operation without necessarily making meaningful progress toward the user's intended objective. Conversely, an outcome may already be sufficient even when additional potentially relevant information or computational operations remain available. IDEBD therefore proposes that execution termination should be determined by the relationship between user intent and expected incremental outcome improvement rather than by the mere availability of further actions. The paper develops the framework's architecture, conceptual decision model, execution boundary mechanisms, research propositions, potential advantages, evaluation methodology, and implementation considerations. It also examines the relationship between execution boundary detection and autonomous task orchestration, task completion verification, resource allocation, human oversight, and autonomous agent reliability. The proposed framework is presented as a conceptual research contribution and does not claim completed experimental validation or demonstrated performance superiority.