Intent to Execution: An Accountability Pattern for Consequential Software Actions

Most software systems answer two questions well: was the caller permitted to do this? (authorization) and did something happen? (logging). Between them sits a third that fewer systems can answer: can we reconstruct why an action was permitted, whether anyone affirmed it, under which policy version, with what scoped authority, and whether what executed matched what was authorized? As consequential actions are increasingly proposed by services, workflows, and autonomous agents that cannot later explain themselves, the record must carry the explanation or it is lost.This paper names that accountability gap and presents Intent to Execution, a stack-neutral pattern that treats the path from a proposed action to a performed one as an explicit sequence of responsibilities: intent captured as data, policy context, constraint evaluation, an explicit decision, an optional recorded acknowledgment, durable audit residue, scoped follow-on authority, host-owned execution, and reconciliation. Within the pattern we develop the Dynamic Liability Handshake (DLH): a first-class, structured request-and-acknowledgment primitive that records an affirmation of intent before a high-stakes act.We describe a .NET reference implementation, AsiBackbone, that realizes the pattern as framework-neutral primitives with host-owned execution, and we map the pattern's elements onto independently published 2026 agentic-AI governance guidance. We are deliberate about scope: the pattern provides accountability structure, not enforcement, tamper-evidence, or compliance certification, and execution remains the host's responsibility.This is a preprint experience-and-pattern paper. The reference implementation is openly available under the MIT license at https://github.com/cdcavell/AsiBackbone and archived on Zenodo (DOI: 10.5281/zenodo.20546032).