From the course: Agentic AI: A Framework for Planning and Execution
Join today to access over 24,600 courses taught by industry experts.
How agents differ from AI/ML models
From the course: Agentic AI: A Framework for Planning and Execution
How agents differ from AI/ML models
- So now that we've explored what an agent is and we've looked at their key characteristics of autonomy, goal orientation, reactivity, and persistence, let's compare and contrast these agent capabilities with traditional AI and machine learning models to highlight the fundamental differences between them. Traditional AI and ML models are designed with a specific focus, pattern recognition and prediction based on what they learned from historic data. These models excel at tasks like classifying images, forecasting trends, or parsing basic language. But they do operate within a constrained framework. When you take a look at a typical ML model, what you're seeing is essentially a sophisticated input-output system. Data goes in, predictions come out. There's no initiative, no autonomous decision-making, and typically no memory of past interactions beyond what's encoded in their training data. In the past, many developers overcame that goal with frameworks and applications to orchestrate…
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.