Runtime configuration for situated governance of AI agents: a case study in investigative journalism

Abstract

AI agents increasingly enter practitioner workflows through delegated, multi-step tasks, such as data analysis, document review, coding, and summarization. Existing governance debates tend to emphasize provider-level technical governance, which steers general model behavior, and policy, which defines the boundaries of legitimate use. Both are necessary, but neither fully specifies how domain-specific norms should guide the intermediate choices agents make during task execution. This article develops runtime configuration as a meso-level, agent-facing governance mechanism for this operational gap. Runtime configuration refers to persistent, inspectable, and revisable instructions and supporting materials loaded at use time that specify decision authority, documentation and evidence-preservation duties, and conditions for human escalation. These artifacts bridge domain practice and agent execution. They translate situated normative commitments into agent-facing guidance while connecting that guidance to technical controls, work outputs, and human review. We illustrate the framework through a case study of investigative journalism, comparing three conditions: an unconfigured baseline and two configured conditions that guided agent runs on a public-records data task. Across the runs, the clearest differences associated with configuration concerned the conditions of delegation rather than substantive accuracy: The runs differed in escalation, provenance, workflow recoverability, and the visibility of consequential decisions. The aim of runtime configuration is not to replace model alignment, policy, expertise, or institutional accountability. Instead, it makes situated delegation more inspectable by translating normative domain commitments into operational guidance for agentic work.