Intent-Driven Task Completion Semantics: Defining and Verifying Successful Outcomes in Autonomous Internet Execution

The increasing development of generative artificial intelligence, autonomous agents, and intent-driven Internet systems has created new possibilities for executing complex digital tasks through natural-language instructions. Autonomous systems can increasingly interpret user requests, discover available capabilities, decompose objectives, invoke external services, and coordinate heterogeneous computational resources. However, an important conceptual problem remains insufficiently addressed: successful execution of computational operations does not necessarily imply successful completion of the user's underlying intention. An agent may successfully invoke an API, retrieve information, execute a workflow, or generate a final response while producing an outcome that fails to satisfy the user's actual requirements. This paper proposes Intent-Driven Task Completion Semantics (IDTCS), a conceptual and formal framework for defining, evaluating, and verifying successful outcomes in autonomous Internet execution. Rather than treating tool execution, procedural completion, or response generation as sufficient evidence of task completion, IDTCS defines completion in terms of the satisfaction of explicitly represented user intentions, applicable constraints, and outcome-validity conditions. The proposed framework introduces six interconnected theoretical components: Intent Satisfaction Conditions, Goal Completion Semantics, Partial Satisfaction, Constraint Compliance Verification, Outcome Validity, and Completion Confidence. It further introduces False Completion Detection to identify situations in which an autonomous system reports a task as completed despite insufficient evidence that the intended outcome has been achieved. A formal task representation is developed to distinguish mandatory requirements, optional preferences, contextual conditions, and verifiable outcome properties. Based on this representation, the framework defines a multidimensional completion model that distinguishes execution status from intention satisfaction and outcome validity. It also introduces a verification-oriented execution process in which completion decisions are evaluated against explicit satisfaction conditions rather than inferred solely from successful computational operations. The paper further examines the potential advantages of IDTCS, including reduced false completion, improved execution reliability, more meaningful partial-task reporting, stronger constraint preservation, improved auditability, and greater compatibility with heterogeneous autonomous Internet capabilities. It explores how the proposed semantics could support intent-driven task orchestration, cross-platform execution, autonomous workflow recovery, and trustworthy AI agents. Importantly, IDTCS is proposed as a conceptual and formal research framework rather than an experimentally validated system. Its theoretical definitions, verification mechanisms, and evaluation methodology are intended to provide a foundation for subsequent implementation and empirical investigation. The central proposition is that autonomous task completion should be defined by the validity of achieved outcomes relative to intended objectives, rather than by the successful execution of computational procedures alone.