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the future is billions of AI agents

Agentic AI vs Regular AI: What's the difference?​

Agentic systems dynamically and autonomously adjust their approaches to meet their goals,  whereas regular AI systems do not because they lack autonomy. Non-agentic AI systems include non-autonomous generative AI and analytical/predictive models that also lack autonomy. Non-agentic AI/ML systems typically learn parameter rules from the data itself but do not have any level of autonomy. However, Agentic AI systems can assess their environments and make informed decisions about the best next course of action towards their goals with some level of autonomy.

Taking into account our example of managing inventory for our shoe retailer above, here's how a regular AI and an Agentic AI would manage tasks differently:

  • Regular AI: A traditional AI system might generate demand forecasts or suggest optimal inventory levels based on predefined models or historical data, but it would require human intervention to make decisions, place orders, or adjust strategies. For example, a regular AI could be set up to ping the supply chain manager when demand for a particular shoe style is rising, but the manager would have to take initiative and act on that information to order more of those shoes to meet demand.

  • Agentic AI: An Agentic AI would theoretically be able to independently and autonomously assess the current stock situation, decide how much inventory is needed to meet demand, place orders with suppliers, reroute deliveries, and even adjust pricing if necessary.

The 7 levels of agency in AI systems​

Level of agencyExplanation
1.Reactive (non-agentic)The AI responds to specific, predefined triggers or commands. It acts only when prompted by external inputs, without long-term goals or independent decision-making.
2.Assistive (non-agentic)The AI provides recommendations or analysis (e.g., forecasting, optimization suggestions) but requires human intervention to make final decisions and take actions.
3.Semi-autonomousThe AI can perform certain tasks or decisions independently within defined parameters. For example, it might adjust inventory levels but still require a human in the loop to approve high value or large scale actions.
4.Autonomous executionThe AI autonomously executes tasks without human intervention, such as placing orders with suppliers or managing logistics. However, its actions are bound by predefined rules or constraints set by humans.
5.Autonomous adaptabilityThe AI adapts its actions based on changing conditions (e.g., rerouting shipments due to weather or supplier delays) and learns from past experiences to improve future performance. It still operates within general guidelines set by humans.
6.Goal-oriented autonomyThe AI autonomously sets and pursues long-term goals (e.g., optimizing supply chain efficiency), adjusts strategies dynamically, and interacts with multiple systems or agents. It continuously learns and adapts without needing human input for decision-making.
7.Full agencyThe AI independently identifies problems, sets goals, and adapts in real time, managing all aspects of a given domain. It operates across complex systems and can negotiate or collaborate with other AI or human agents to achieve its objectives, with minimal or no human oversight. Fully Agentic AI systems are self-governing.