Jul 2026· Universal Library of Engineering Technology· Vol 3, pp. 13-18· 0 citations· 12 references
TL;DR
This review examines the two approaches to embedded assistance through a seven-layer reference architecture that places each in a specific tier and clarifies why the boundary between them lies between the model and orchestration tiers.
Abstract
Customer relationship management platforms now embed artificial intelligence in two structurally different ways, and the choice between them shapes cost, governance, and the pace of organizational change. The first way places assistive models within the interfaces teams already use, so that scoring, summarization, and drafting occur at the point where work is performed. The second way introduces a separate orchestration tier in which semi-autonomous agents pursue goals across several systems while users remain in familiar screens. This review examines the two approaches through a seven-layer reference architecture that places each in a specific tier and clarifies why the boundary between them lies between the model and orchestration tiers. The analysis treats both as design options with measurable trade-offs across change management, expressiveness for multi-step work, architectural coupling, and the surface available for monitoring. Evidence from enterprise practice and the research literature suggests that embedded assistance is more successful during first-generation adoption in mature organizations because it leverages existing processes, whereas a dedicated agent tier yields stronger long-term outcomes for second-wave programs that target genuine cross-system automation. Supporting comparisons across data access, integration style, the trust model, and migration strategy show that the central decision sits inside a wider system of architectural commitments that the framework makes explicit for practicing architects.
Platform engineering has become, over the past several years, the default way large organizations manage the growing complexity of software delivery. That arrival has coincided with a second, less discussed obligation. Platform teams must now build platforms that host AI workloads, retrieval-augmented generation services, model inference endpoints, autonomous agents, while also embedding AI into the platform's own control plane to manage the infrastructure itself. This article calls the pairing a dual mandate and argues, from eighteen years spent migrating enterprise build and deployment infrastructure through several earlier paradigm shifts, that the two obligations are not separable line items but one reinforcing system. Neither the choice of model nor the surrounding tooling binds them together; governance does: provenance, scoped authority, bounded autonomy, and measurement. Drawing on recent platform-engineering, MLOps, and AI-governance literature alongside the author's own experience navigating prior infrastructure transitions, from sequential build systems to distributed CI/CD, from manual provisioning to Infrastructure as Code, this article defines the dual mandate precisely, examines each half in turn, explains why governance becomes the binding constraint at their intersection, and proposes a reference architecture a platform team can apply directly. The analysis is offered as a synthesis of published literature and the author's cross-paradigm operational experience, not as an empirical study of a specific AI production deployment. Where the discussion extends into AI-workload specifics beyond that direct experience, this is stated as informed extrapolation from platform-engineering fundamentals rather than implied first-hand practice.
Sonu Kumar· International journal of com...· 0 citations
Hospitals are racing to embed Artificial Intelligence (AI), while coping with the surge in adaptation of the
technology in other industries, into the triage management, documentation, scheduling, and revenue-cycle workflows, yet
most deployments remain as fragmented pilots that stall at the edge of production, exposing patients and institutions to
operational fragility, ungoverned risk, and mounting technical debt. At the same time, the global AI-in-healthcare market is
projected to exceed nearly USD 1 trillion by 2034, according to the report of Fortune Business Insights, amplifying the
financial consequences of architectural missteps and failed scaling strategies. This research proposes a compliance-first
Agentic AI pattern catalogue and orchestration framework, purposely built for Hospital Information Management Systems
(HIMS), moving beyond the single Large Language Model (LLM) chatbots and towards a governed ecosystem of
autonomous and semi-autonomous agents. The framework extends by adding (i) a taxonomy of Agentic roles
(conversational, orchestration, reconciliation, auditing, and decision-support agents), (ii) a formal risk-stratification model
that maps each pattern to risk tiers, human-in-the-loop checkpoints, and governance hooks, and (iii) a unified orchestration
runtime capable of coordinating multi-agent workflows across EHR/HIMS landscapes such as Epic, Cerner, and
MEDITECH. Technically, the framework combines vLLM (Virtual Large Language Model)-based inference, optimized
paging memory, confidential computing, and Model Context Protocol (MCP) based on-premise deployment, enforcing endto-end encryption and policy-as-code controls aligned with HIPAA, GDPR, the EU AI Act, India’s DPDP and DISHA Acts,
ISO 27001, ISO 27002, ISO 14971, and IEC 62304. Using synthetic but structurally realistic and reflecting the complexities
of the hospital data generated using Synthea, and controlled pilot deployment and functional run, we exhibit how the
proposed architecture is capable and efficient to reduce the documentation time, integration effort, and AI pilot attrition
while constriction the governance and auditability, offering hospital leaders and governing authorities an urgently needed
blueprint to convert AI investment into sustainable clinical, operational, and financial ROI.
Manideep Dhar, Ritwik Singh, Sharat Chandra Kumar Manikonda· International Journal of Inn...· 0 citations
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.
Nick Hagar, Nick Diakopoulos· AI and Ethics· 0 citations
Large language models (LLMs) have evolved from standalone generative systems into agentic AI systems capable of planning, reasoning, tool use, and multi-agent collaboration. Enterprises are increasingly adopting AI agents to automate and orchestrate complex workflows, from IT operations to employee productivity. While early deployments focused on proof-of-concept prototypes, the past year has marked a clear shift toward production-grade enterprise AI agents. This transition has been enabled by a wave of new technologies, including multi-agent orchestration, memory and state management, skill-based and modular agent architectures, and deeper integration with enterprise data and workflow platforms, which together make scalable, reliable agent systems feasible in practice. At the same time, moving agents into production introduces new technical and organizational challenges, such as rigorous evaluation and benchmarking, security and governance, and system design for long-running, autonomous operation. Building on the success of our two prior highly attended editions: ''Agentic AI for Enterprise'' workshop at KDD 2025 and ''Enterprise RAG'' workshop at CIKM 2024, this workshop aims to bring together researchers and practitioners to examine how enterprise AI agents can successfully move from prototypes to production. We focus on three pillars: 1) Agent architectures and systems; 2) Enterprise applications and deployments; 3) Evaluation and governance.
Min Du, Anbang Xu, Jasmine Jaksic et al.· Proceedings of the 32nd ACM...· 0 citations
It is argued that agentic AI governance is four problems, not one, each with a mature governing science, and a five-level maturity model with a non-compensatory bottleneck-weighted index and assessment instrument operationalizes CASE as a scientific rather than process maturity model, grounded in production enterprise agentic platforms.
Srinivas Telukunta, Georgios Nektarios Lilis, Lucio Baron· 0 citations
The ORBIT (Operations Responses and Business Intelligence Toolkit) project was initiated to assess agentic AI for the upcoming ESnet 7 initiative and to address persistent operational pain points in the Network Operations Center (NOC) workflow. ESnet operators experience slow retrieval from siloed data sources, incidents described in lengthy and difficult-to-parse tickets, and context loss across shift handoffs. These challenges increase cognitive load and prolong incident resolution times. ORBIT therefore targets routine automation, cross-source synthesis, and actionable insights delivered directly within operators'existing tooling. ORBIT is an agentic AI system integrated into ServiceNow, ESnet's primary incident management platform. The design uses a modular, layered architecture comprising a centralized reasoning hub, tool access via MCPs for ESnet data sources, a semantic search layer, and an operator-facing chat interface. To manage the complexity and stochasticity of the AI toolchain, ORBIT follows industry best practices by structuring task logic as versioned, tested"skills"that guide the system in performing bounded responsibilities. This improves reliability and predictability compared to fully unconstrained agent behavior. Key results show that ORBIT successfully delivered all six initial tasks, and the architecture enabled rapid development of two additional tasks proposed by NOC engineers. We observed strong organic adoption of general-purpose infrastructure components, especially the chat interface and LiteLLM model gateway, including high request volumes from outside the project. Experiments with skills indicate that this approach can reduce task completion steps while eliminating observed error modes.
BinHao Dong, Sukhada Gholba, Brooklin Gore et al.· arXiv.org· 0 citations
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