Asynchronous Result Pipelines in C#: Task> and LINQ-Based Monadic Composition for AI Applications

This technical note defines a practical asynchronous Result pipeline pattern for C# using Task> and LINQ-based monadic composition. It focuses on AI application pipelines where predictable failures such as model refusal, schema mismatch, parsing failure, policy denial, timeout, and provider-side validation errors should be propagated as structured values rather than uncontrolled exceptions. The paper introduces Result with ErrorContext, Select and SelectMany extensions for LINQ query syntax, fail-closed pipeline behavior, exception absorption boundaries for unexpected runtime failures, and Nullable Flow Analysis support using MemberNotNullWhen. It demonstrates how predictable domain failures can stop downstream computation safely while preserving composability, determinism, and developer ergonomics. The paper positions this pattern as a practical implementation discipline aligned with Interface-Led Architecture and AIKernel.Common. It distinguishes predictable AI pipeline failures from unexpected infrastructure exceptions and explains why AI failure should be treated as context rather than as a raw string. The English manuscript is the canonical version. The Japanese manuscript is included as a companion translation.