Eranga et al. 2025 and Patra et al. 2026 independently converge on separation of concerns as the foundation of auditable production agentic systems: Eranga prescribes single-responsibility agents and clean separation between workflow logic and MCP servers, while Patra assigns each architectural layer a clear responsibility so governance checks stay visible and testable.
E5 verbatim supports Eranga prescribing single-responsibility agents and clean separation between workflow logic and MCP servers, and E12 verbatim supports Patra giving each architectural layer a clear responsibility so governance checks stay visible, testable, and auditable.
Written by Kimi K3 via Ollama Cloud · checked by GLM-5.3 via Ollama Cloud · 1 Oct, 04:35
Source chain
AEvery quote below was checked, without a model, to appear verbatim in its source.
- 01
“single-tool and single-responsibility agents, externalized prompt management, ResponsibleAI-aligned model-consortium design, clean separation between workflow logic and MCP servers”
Full-text passage · p. 2 · evidence E5
ing, developing, and deploying production-quality agentic AI systems. We introduce a structured engineering lifecycle encompassing workflow decomposition, multi-agent design patterns, Model Context Protocol(MCP), and tool integration, deterministic orchestration, Responsible-AI considerations, and environment-aware deployment strategies. We then present nine core best practices for engineering production-grade agentic AI workflows, including tool-first design over MCP, pure-function invocation, single-tool and single-responsibility agents, externalized prompt management, ResponsibleAI-aligned model-consortium design, clean separation between workflow logic and MCP servers, containerized deployment for scalable operations, and adherence to the Keep it Simple, Stupid (KISS) principle to maintain simplicity and robustness. To demonstrate these principles in practice, we present a comprehensive case study: a multimodal news-analysis and media-generation workflow. By combining architectural guidance, operational patterns, and practical implementation insights, this paper offers a foundational reference to build robust, extensible, and production-ready agentic AI workflows.
Passage read from arxiv.org, which may be a preprint rather than the published version.
- 02
“each layer is given a clear responsibility. This separation helps in keeping important governance checks like consent validation, bias monitoring, and confidence evaluation visible and testable”
Full-text passage · no page number · evidence E12
Passage read from www.ebi.ac.uk, which may be a preprint rather than the published version.