Moving beyond the buzzwords. A visual framework for understanding, evaluating, and classifying the true capabilities of intelligent systems.
Systems driven by a purpose, actively working towards a specific objective rather than passively responding to commands.
The capacity to operate and execute complex, multi-step tasks with limited or no direct human intervention.
The ability to learn from experience, adjust strategies in response to new information, and improve performance over time.
Takes initiative to perceive its environment and act upon it, often using external tools like APIs to achieve its goals.
Agentic behavior isn't a single action, but a continuous, iterative cycle. This loop is what enables an AI to think, act, and learn in a dynamic world.
Not all "agents" are created equal. This framework measures capability across five key dimensions, creating a unique "fingerprint" for any AI system. Here's how three real-world use cases compare, rated on a scale of 0 (Non-Agentic) to 4 (Fully Autonomous).
Understanding the key differences between agentic systems and other AI paradigms is crucial for proper classification and expectation setting.
| Feature | Generative AI | Reactive/Rule-Based | Agentic AI |
|---|---|---|---|
| Primary Function | Content Creation | Task Execution (Fixed Rules) | Goal-Oriented Action |
| Decision Logic | Probabilistic Generation | If-Then Conditions | Dynamic Planning & Reasoning |
| Example | Generating an email draft | An automated billing system | An autonomous supply chain manager |