Agentic AI marks a major shift in how autonomous systems reason, plan, and execute multi-step tasks. Unlike traditional single model prompting, agentic workflows integrate multiple specialized agents with different Large Language Models(LLMs), tool-augmented capabilities, orchestration logic, and external system interactions to form dynamic pipelines capable of autonomous decision-making and action. As adoption accelerates across industry and research, organizations face a central challenge: how to design, engineer, and operate production-grade agentic AI workflows that are reliable, observable, maintainable, and aligned with safety and governance requirements. This paper provides a practical, end-to-end guide for design-Email addresses: cmedawer@odu.edu 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, Responsible-AI-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. Introduction The rapid advancement of Large Language Models (LLMs) [1, 2], Vision-Language Models (VLMs) [3, 4, 5], and tool-augmented reasoning has laid the foundation for a new paradigm in automation: agentic AI [6, 7]. Traditional LLM interactions follow a simple pattern in which a human provides a prompt and the model generates a response (as illustrated in the top half of Figure 1).

Cybersecurity firm RiskSense, which has been at the forefront of diagnosing persistent threats for many years, on Tuesday announced a dashboard to warn companies how much they may be at risk from various kinds of ransomware. The service, available as an update to the company's subscription-based SaaS software, is a visual monitor that shows various data, such as the number of vulnerabilities found across an enterprise's systems. It can be used not only to assess the situation but to plan a sequence of steps to remediate the matter. The dashboard is based on signals coming from typical enterprise security monitors such as those sold by Rapid7. CEO and co-founder Dr. Srinivas Mukkamala told ZDNet the intention at some point is to fold into the product neural network capabilities for additional kinds of analysis such as regression analysis.

WIRE)--RiskSense, Inc., pioneering risk-based vulnerability management and prioritization, today announced that its CEO, Dr. Srinivas Mukkamala will lead an expert panel at the SINET Showcase conference in Washington, DC on November 7, 2019 on the impact of bias in AI-driven security systems. Dr. Srinivas Mukkamala, co-founder and CEO of RiskSense, is a recognized expert on artificial intelligence (AI) and neural networks. He was part of a think tank that collaborated with the U.S. Department of Defense and U.S. Intelligence Community to apply these concepts against cybersecurity problems. Dr. Mukkamala was also a lead researcher for CACTUS (Computational Analysis of Cyber Terrorism against the U.S.) and holds a patent on Intelligent Agents for Distributed Intrusion Detection System and Method of Practicing. Artificial intelligence and machine learning are increasingly being used and trusted by organizations to automate security threat detection.

Add one more to the growing tally of security funding deals in early 2017. RiskSense, an Albuquerque, NM cyber-risk management company, announced this week that it had raised $14 million in a Series A round of financing. "The funding raised by existing investors Paladin Capital Group, Sun Mountain Capital, EPIC Ventures, and CenturyLink and a new investor Jump Capital will enable RiskSense to expand sales and marketing, enter new markets such as cyber-security insurance, and broaden and accelerate product development," Dr. Srinivas Mukkamala, co-founder and CEO of RiskSense, told eSecurity Planet. Spun off from New Mexico Institute of Mining and Technology and acting as advisors to the U.S. Department of Defense and U.S. Intelligence Community, RiskSense uses of artificial intelligence (AI) technologies, particularly machine learning, to help governments and enterprise organizations identify and prioritize risks to their networks and data. "RiskSense is changing the way organizations detect and manage cyber risk," said Mukkamala.