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

Stop Building HR Agents: Why Workflows Beat Agentic AI for Most People Functions

Organizations are racing to deploy agentic AI systems across human resources functions, driven by vendor hype and fear of competitive disadvantage. However, most HR use cases labeled "agentic" are actually deterministic workflows with inflated costs and unnecessary complexity. This article examines the critical distinctions between AI tasks, workflows, and autonomous agents in HR contexts, drawing on implementation evidence and practitioner experience to establish decision frameworks for technology selection. Research on algorithmic management, procedural justice, and system trust reveals that autonomous agent deployment often creates more problems than it solves—particularly around cost control, auditability, bias detection, and stakeholder acceptance. Through analysis of real-world HR implementations and recent guidance from AI system architects, we present four diagnostic questions that help practitioners determine when workflows outperform agents: task complexity, economic justification, AI capability alignment, and error tolerance. The evidence suggests that well-governed, human-supervised workflows deliver superior outcomes for approximately 80–90% of current HR AI applications, reserving true agentic systems for genuinely complex, high-value scenarios where dynamic decision-making justifies increased cost and reduced control.

A Comparative Evaluation of AI Agent Orchestration Frameworks for Regulated Environments

Organizations operating under compliance mandates increasingly rely on AI agents to automate workflows involving unstructured documents and dynamic decision-making. In such settings, agentic systems must reconcile autonomy with strict requirements for auditability, controlled variability, and integration with legacy infrastructures. We propose an Exemplar as a reusable evaluation artifact for analyzing agentic orchestration frameworks under Compliance Management and Documentary Uncertainty. The Exemplar captures the interaction between probabilistic extraction and deterministic constraint enforcement in workflows characterized by unstructured inputs, rigid schemas, and asynchronous processes. We empirically evaluate four frameworks (CrewAI, Embabel, LangChain, and n8n) across three dimensions: type safety, reasoning auditability, and legacy interoperability. The evaluation is grounded in a real-world instantiation within a state penitentiary agency, involving automated processing of employment contracts with incomplete data and asynchronous stakeholder interactions. Results show clear trade-offs between flexibility and control, highlighting the importance of hybrid approaches that combine agentic reasoning with deterministic validation. The exemplar generalizes to regulated domains such as public procurement, finance, and healthcare.

Where Accountability Lives: Mapping Human Responsibility to Workflow Artifacts in Agentic Software Development

Coding agents author commits, open pull requests, and push code in production repositories. Who is accountable is settled in two places that do not refer to each other: the platform controls that gate what an agent may do, and the provider terms that allocate responsibility for what it produces. We read both against the workflow events that leave artifacts, across four agentic coding tools and eighteen governing policy documents from seven providers, recording at each event who holds authority, who executed and under which identity, who must verify, who bears the consequence, and which artifact survives. The layers disagree. One provider bars the developer who assigned a task from approving the resulting pull request; another documents an agent that approves pull requests below a configured risk threshold and can dismiss reviews. We therefore replace the usual three-way distinction between enforced, advisory and absent verification with a grid separating whether a mechanism compels the check from who performs it. Attribution runs in opposite directions across providers, and no trailer is defined for agent authorship, though one provider repurposes the co-authorship trailer for it. We argue that agentic tooling did not create this gap. A decade of code-review research already recorded that the approval artifact carries less than the terms assume. What changes is that this weakness moved from a property of how people work to a property of what a product does: a vendor now documents a product that stands at the approval event and emits the same artifact with no party capable of forming a judgement present. We do not claim the gap harms anyone. The selection rule is equalised across the four tools, every reported absence is re-tested against a doubled page set with the survival rate reported, and the source collection and its scripts are deposited.

The Governance Gap in Contemporary LLM-Based Agentic Systems: A Structural Diagnostic Review

Large Language Models (LLMs) are increasingly integrated into agentic workflows that require extended reasoning, persistent state management, coordinated tool use, and controlled execution. As this operational scope expands, a central question emerges: whether probabilistic generation alone can reliably support coherent behavior across interacting system components. This paper addresses that question through a structural diagnostic review of contemporary agentic systems. Starting from LLM-based tutoring as an analytically demanding entry point and extending toward structurally related agent architectures, the paper draws on a five-phase review of N=145 research records. The analysis is organized through the Agentic Structure Taxonomy (AST), which structures the literature across four dimensions: Cognition, Interaction, Orchestration, and Governance. The review identifies five recurrent empirical problem patterns and uses them as abductive diagnostic cues for formulating seven cross-dimensional transition gaps that capture recurrent discontinuities at the boundaries between reasoning, state, control, and execution. From these gaps, fourteen structural constraints are derived across three control domains: state isolation, control alignment, and execution governance. These constraints are interpreted not as prescriptive design mandates, but as analytically derived conditions associated with reducing error propagation across subsystem transitions. The paper argues that reliability in agentic systems is shaped not only by model performance or prompt design, but also by whether the boundaries linking probabilistic reasoning to persistent state, orchestration, and execution are governed by explicit structural conditions.

Making Agent-Mediated Contributions Governable: A Project-Level Governance Manifest for Open-Source AI Collaboration

The study develops a three-layer framework of agent-readability, traceability, and governability, theorizes agent-mediated contributions as governable boundary objects, and advances compliance-enabling digital innovation governance while preserving maintainer decision authority.

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