Agentic AI is everywhere right now — but what actually makes it different from traditional automation? And more importantly, when should you use AI agents versus deterministic workflows? In this video, we break down the evolution from traditional machine learning and generative AI to Agentic AI, explaining how AI agents reason through problems, use tools to take action, and orchestrate complex workflows to achieve business outcomes. Whether you’re just starting your AI journey or evaluating how AI Agents fit into your ServiceNow strategy, this video provides a practical framework for understanding what agents are, how they work behind the scenes, and when they actually deliver value in the enterprise. —————————————————————————— ⏱ TIMESTAMPS —————————————————————————— 00:00 - Traditional AI vs Generative AI 01:28 - Introduction to generative AI 03:14 - AI Agent architecture at ServiceNow 04:38 - Workflows vs AI Agents —————————————————————————— 📌 WHAT YOU’LL LEARN —————————————————————————— ✅ The differences between Artificial Intelligence, Machine Learning, Generative AI, and Agentic AI ✅ How Large Language Models (LLMs) generate responses using context and retrieval techniques ✅ Why Retrieval-Augmented Generation (RAG) improves AI accuracy and reliability ✅ How AI agents use Thought → Action → Observation loops to solve problems ✅ The core components of ServiceNow’s multi-agent architecture ✅ How orchestrators, worker agents, and tools collaborate to achieve goals ✅ Why business outcomes should drive AI adoption decisions ✅ When to use AI Agents versus traditional workflows and assistive AI capabilities ✅ The trade-offs between agentic systems and deterministic automation —————————————————————————— 🏗️ SERVICENOW AGENTIC AI ARCHITECTURE —————————————————————————— This video explores how ServiceNow approaches Agentic AI using a multi-agent framework designed to solve enterprise problems through intelligent orchestration. Key concepts covered: 🤖 AI Agents — autonomous systems that reason through complex goals 🧠 Orchestrators — coordinate work and assign tasks across specialized agents 🔧 Worker Agents — focused agents designed to perform specific functions 🛠️ Tools — scripts, flows, APIs, and integrations that agents use to take action ⚡ Triggers — user interactions or system events that initiate workflows Using a practical house-building analogy, the video demonstrates how orchestrators function like general contractors, coordinating specialized teams and resources to accomplish complex objectives. —————————————————————————— 💡 KEY TAKEAWAYS —————————————————————————— AI Agents are not the goal — business outcomes are. Organizations succeed with AI when they focus on measurable outcomes such as: 💰 Increasing revenue 📉 Reducing operational costs 🛡️ Lowering business risk 😊 Improving customer and employee experiences AI Agents excel at solving ambiguous, dynamic problems where adaptability and reasoning are required. Traditional workflows and assistive AI remain the better choice for stable, deterministic processes that require speed, predictability, and lower operational costs. The key is knowing when to use each approach. —————————————————————————— 🔗 RELATED VIDEOS IN THIS SERIES —————————————————————————— Check out the other videos in this series where we explore ServiceNow AI Agents, Agentic AI architectures, Knowledge Graphs, AI Agent Studio capabilities, and practical enterprise use cases to help you build effective AI-powered solutions. —————————————————————————— 🏷 ABOUT THIS SERIES —————————————————————————— This series explores Agentic AI in ServiceNow — covering the concepts, architectures, tools, and implementation strategies needed to build enterprise-ready AI solutions. Designed for ServiceNow architects, developers, administrators, and business leaders looking to understand how AI Agents create real business value. #ServiceNow #AIAgents #AgenticAI #NowAssist #GenerativeAI #MachineLearning #EnterpriseAI #AIAutomation #ServiceNowDeveloper #DigitalTransformation