Eranga et al. 2025 present their containerized, Kubernetes-orchestrated, MCP-accessible system with Responsible-AI mechanisms as a robust template that organizations can adapt across domains such as compliance automation, media generation, analytics, and enterprise RPA.
E6 states verbatim that by integrating containerization, Kubernetes orchestration, MCP accessibility, and Responsible-AI mechanisms, the proposed system offers a robust template adaptable across domains such as compliance automation, media generation, analytics, and enterprise RPA.
Written by Kimi K3 via Ollama Cloud · checked by GLM-5.3 via Ollama Cloud · 1 Oct, 04:35
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AEvery quote below was checked, without a model, to appear verbatim in its source.
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“By integrating containerization, Kubernetes orchestration, MCP accessibility, and Responsible-AI mechanisms (bias mitigation, reasoning audits, deterministic operations)”
Full-text passage · p. 5 · evidence E6
4.An extensible blueprint for organizations adopting agentic AI in production.By integrating containerization, Kubernetes orchestration, MCP accessibility, and Responsible-AI mechanisms (bias mitigation, reasoning audits, deterministic operations), the proposed system offers a robust template adaptable across domains such as compliance automation, media generation, analytics, and enterprise RPA. The remainder of the paper is organized as follows. Section 2 presents the motivating use case, showcasing an end-to-end agentic AI workflow for multimodal news analysis, content synthesis, and media generation. Section 3 builds upon this use case by introducing a curated set of best practices for designing, developing, and deploying production-grade agentic AI workflows, with emphasis on architectural choices, orchestration patterns, tooling strategies, and Responsible-AI–aligned design principles. Section 4 details the implementation of the proposed workflow—including agent design, tool/function integration, reasoning-based consolidation, multimodal media generation, and deployment in a containerized production environment.
Passage read from arxiv.org, which may be a preprint rather than the published version.
- 02
“the proposed system offers a robust template adaptable across domains such as compliance automation, media generation, analytics, and enterprise RPA”
Full-text passage · p. 5 · evidence E6
4.An extensible blueprint for organizations adopting agentic AI in production.By integrating containerization, Kubernetes orchestration, MCP accessibility, and Responsible-AI mechanisms (bias mitigation, reasoning audits, deterministic operations), the proposed system offers a robust template adaptable across domains such as compliance automation, media generation, analytics, and enterprise RPA. The remainder of the paper is organized as follows. Section 2 presents the motivating use case, showcasing an end-to-end agentic AI workflow for multimodal news analysis, content synthesis, and media generation. Section 3 builds upon this use case by introducing a curated set of best practices for designing, developing, and deploying production-grade agentic AI workflows, with emphasis on architectural choices, orchestration patterns, tooling strategies, and Responsible-AI–aligned design principles. Section 4 details the implementation of the proposed workflow—including agent design, tool/function integration, reasoning-based consolidation, multimodal media generation, and deployment in a containerized production environment.
Passage read from arxiv.org, which may be a preprint rather than the published version.