When to Use Agentic AI: A Practical Framework
About this lesson
In this video, we explore how to identify the right use cases for Agentic AI — and just as importantly, when not to use it. Agentic AI is powerful, but it is also complex, non-deterministic, and harder to control than traditional techniques. This lesson gives you a practical decision framework for choosing between agentic AI, workflows, RPA, machine learning, optimization, and other approaches. You’ll learn how to recognize problems that truly require agentic behavior, understand the tradeoffs involved, and avoid common mistakes that cause real-world AI projects to fail. Timestamps: 0:00 - Introduction to agentic AI applications 0:16 - Why agentic AI should be used as a last resort 0:39 - How to spot real agentic AI use cases 0:58 - Comparing agentic AI to RPA, ML, and optimization 1:39 - The “smart assistant” mental model 2:07 - Tradeoffs: compliance, privacy, and determinism 2:38 - Why explainability matters in high-stakes systems 3:51 - A real-world lesson from a failed complex system Watch this video if you’re deciding whether to use agentic AI in a real project, designing AI systems for business applications, or trying to avoid overengineering solutions with unnecessary autonomy. This video is part of the Agentic AI Developer Certification Program by Ready Tensor. Enroll Now: https://www.readytensor.ai/agentic-ai-essentials-cert/ About Ready Tensor: Ready Tensor helps AI and ML professionals build real-world, production-minded agentic AI systems through hands-on certifications, portfolio projects, and practical engineering guidance. Learn more: https://www.readytensor.ai/ Like the video? Subscribe for more practical lessons on agentic AI system design, tradeoffs, and production realities.
DeepCamp AI