Intent-Driven Computational Reproducibility: Reconstructing and Replaying Autonomous Internet Task Execution

The increasing development of generative artificial intelligence, autonomous agents, and intent-driven Internet systems is transforming how computational tasks are performed across heterogeneous digital environments. Complex Internet tasks increasingly involve multiple search operations, API requests, data transformations, model inferences, service interactions, decision-making procedures, and automated result generation. Although these systems can execute sophisticated tasks with limited human intervention, their execution processes introduce a fundamental challenge: the inability to reliably reconstruct and reproduce previous computational executions after the underlying Internet environment, services, data sources, models, and execution conditions have changed. This paper proposes Intent-Driven Computational Reproducibility (IDCR), a conceptual research framework designed to enable the reconstruction, inspection, and replay of autonomous Internet task executions. Rather than treating computational reproducibility exclusively as the reproduction of scientific experiments or the preservation of software environments, IDCR extends the concept toward autonomous, multi-service Internet tasks whose execution processes may depend on dynamically changing external capabilities. The proposed framework introduces six interconnected mechanisms: Intent Execution Snapshots, Reproducible Execution Manifests, Capability Version Recording, Deterministic Replay, Execution Environment Reconstruction, and Result Difference Analysis. These mechanisms collectively aim to preserve the relationship between an original user intention, the execution plan generated to satisfy that intention, the capabilities invoked during execution, the computational conditions under which each operation occurred, and the resulting outputs. A central feature of IDCR is its distinction between reproducing an execution under its original conditions and re-executing an equivalent task under contemporary conditions. The former seeks to reconstruct the original computational process as accurately as possible, whereas the latter investigates how the same underlying intention behaves when exposed to updated data, services, models, and execution environments. The framework therefore introduces a multi-level reproducibility perspective encompassing execution reconstruction, deterministic replay, controlled re-execution, and comparative outcome analysis. Importantly, IDCR does not assume that all Internet operations can be reproduced exactly. External websites may change, APIs may become unavailable, commercial models may be updated, and certain operations may depend on nondeterministic or inaccessible services. Instead, the framework proposes recording reproducibility constraints, identifying irrecoverable dependencies, distinguishing deterministic from nondeterministic operations, and explicitly reporting the degree to which an original execution can be reconstructed. This paper develops the conceptual foundations, system architecture, execution model, reproducibility mechanisms, potential advantages, research propositions, evaluation methodology, and implementation considerations of IDCR. It positions computational reproducibility as a potential foundational capability for trustworthy, inspectable, and continuously evolving autonomous Internet systems. The paper presents a research framework and does not claim completed experimental validation or demonstrated performance superiority.