What Are Agentic AI Workflows?
In this quick 59-second breakdown, you’ll learn why workflows remain the backbone of reliable AI systems — even in the age of Gen AI and agentic AI.
From pre-coded orchestration to integrating LLMs and SLMs, discover how workflows ensure predictability, reliability, and scalability for production apps.
📌 What you’ll learn:
The difference between traditional ML workflows and Gen AI workflows
How workflows orchestrate AI models through deterministic paths
Why they’re perfect for high-reliability tasks like approvals & summarization
The “pre-fixe menu” analogy that makes workflows easy to understand
📄 Reference Paper: https://arxiv.org/pdf/2504.19678
Chapters
0:00 — What Are Workflows?
0:04 — AI Models by Design
0:10 — Predefined Code Paths
0:16 — Before Gen AI
0:28 — LLMs & SLMs in Workflows
0:38 — The Pre-Fixe Menu Analogy
0:46 — Reliability & Production Use Cases
workflows ai, agentic workflows, gen ai workflows, ai orchestration, deterministic ai, llm integration, slm integration, machine learning workflows, gen ai vs ml, ai reliability, production ai systems, ai system architecture, ai engineering basics, agent vs workflow, ai for product managers, ai automation, ai orchestration tools, langchain workflows, autonomous agents, ai orchestration vs automation, agentic ai, ai planning, llm orchestration, prefix menu ai analogy, ai in production, ai design patterns, ml vs ai workflows, ai process automation, orchestrating llms, orchestrating ai, ai for approvals, ai for summarization, arxiv ai papers, ai product management tips, gen ai engineering, ai developer guide, ai frameworks, ai for business processes, intelligent automation, ai project management, ai model orchestration, ai reliability testing, ai deployment best practices, ai architecture explained, how ai workflows work, ai integration in software, ai in production apps, ai for enterprise automation, agentic systems vs workflows, ai orchestration architecture
Информация по комментариям в разработке