Intent-Driven Digital Capability Reputation: Evaluating the Trustworthiness and Historical Performance of Distributed Internet Services

The evolution of the Internet toward increasingly distributed, heterogeneous, and autonomous digital environments creates new challenges for selecting and coordinating computational capabilities. As generative artificial intelligence, autonomous agents, web services, APIs, and other digital resources become increasingly accessible, an intent-driven Internet may have access to multiple services capable of performing similar tasks. However, the availability of multiple functionally compatible services does not necessarily indicate that they are equally reliable, trustworthy, or appropriate for a particular task. Services may differ substantially in execution quality, historical success rates, response latency, operational stability, output consistency, error frequency, and suitability for specific task categories. Consequently, capability discovery and functional compatibility alone are insufficient foundations for reliable autonomous capability orchestration. This paper proposes Intent-Driven Digital Capability Reputation (IDCR), a conceptual framework for evaluating and maintaining the trustworthiness and historical performance of distributed Internet services according to their observed execution behavior and task-specific suitability. Rather than treating digital capabilities as interchangeable resources based exclusively on their declared functionality, IDCR introduces a reputation-oriented evaluation layer through which capabilities can accumulate, update, and maintain evidence-based performance assessments over time. The proposed framework comprises seven interconnected components: Capability Reputation Modeling, Historical Execution Assessment, Task-Specific Reputation, Multidimensional Reputation Aggregation, Temporal Reputation Decay, Reputation Manipulation Detection, and Cross-Task Reputation Transfer. It further introduces an intent-conditioned capability reputation model that distinguishes general service reputation from task-specific reputation, allowing the same service to receive different suitability assessments depending on the user's intended objective and execution context. A central feature of the framework is the distinction between historical performance and predictive suitability. Historical reputation describes what a capability has demonstrated under previous execution conditions, whereas task-specific reputation estimates how appropriate that capability may be for a particular future task. The framework therefore considers evidence quantity, observation recency, task similarity, contextual compatibility, uncertainty, and potential manipulation when constructing reputation assessments. The paper further examines how digital capability reputation could improve trust-aware capability selection, autonomous task orchestration, service substitution, execution reliability, resource allocation, and accountability in distributed Internet environments. It also develops mathematical formulations, research propositions, evaluation methodologies, and implementation considerations. Importantly, IDCR is proposed as a conceptual research framework rather than a validated operational system. Its potential advantages are theoretically motivated and require further empirical investigation.