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an hour ago - edited an hour ago
Agentic AI
vs. traditional automation
A practical framework for understanding what AI agents are, how they work behind the scenes, and when they actually deliver value in the enterprise.
- 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
This video explores how ServiceNow approaches agentic AI using a multi-agent framework designed to solve enterprise problems through intelligent orchestration. Using a house-building analogy, orchestrators function like general contractors, coordinating specialized teams and resources to accomplish complex objectives.
AI agents are not the goal — business outcomes are. Organizations succeed with AI when they focus on measurable outcomes.
Use AI agents when
The problem is ambiguous and dynamic, and adaptability and reasoning are required.
Use workflows and assistive AI when
The process is stable and deterministic, and speed, predictability, and lower operational cost matter most.
Explore other videos in this series covering ServiceNow AI agents, agentic AI architectures, knowledge graphs, AI Agent Studio capabilities, and practical enterprise use cases for building effective AI-powered solutions.
