I have been reading a lot about the shift from LLMs to agentic systems, but I am struggling to draw a clear line between the two. When I use a standard chat interface to generate text, is that just generative AI, or does it become agentic if I ask it to perform a task?
Specifically, I am curious about the following:
- Do agentic systems require persistent memory?
- Does the ability to call an API automatically make a model agentic?
- Is there a specific threshold where a chatbot crosses into being an agent?
Any technical clarification on the architectural differences would be appreciated.
Agentic AI differs from generative models by incorporating an iterative control loop that observes, plans, and executes actions autonomously to achieve a specific goal, rather than simply predicting the next token in a sequence.
1 answer
A generative model is a probabilistic completion engine, whereas an agentic system is an autonomous control loop designed to reach a state-based objective. Function calling is merely an interface mechanism; true agency requires an iterative observation-thought-action cycle that persists until the task goal is satisfied or the constraints are violated.
Thanks for clearing that up, Mangala Shayana. I usually just lump everything under function calling, but the idea of an autonomous control loop makes way more sense. I’m definitely going to rethink my setup.
Your definition of an agentic system is quite precise, Mangala Shayana. I find the shift from simple probability to state-based objectives rather daunting, yet I appreciate the clarity you provided regarding these complex architectural requirements.
That distinction is profound, Mangala Shayana. I’ve been struggling to categorize these workflows, but your framework clarifies the loop versus the completion engine. I hope I can apply this methodically to my own projects.