Since the inception of Large Language Models, an unprecedented number of new terms have entered our lexicon. The terminology used in tech spheres is coagulating into a single interchangeable buzzword that begins or ends with “AI”.
If you don’t know the difference between the flurry of new AI definitions, then nobody is going to hold it against you. With a new buzzword appearing every week, it’s hard to keep track of the developments within Silicon Valley and beyond.
The technology is shrouded in obscurity. Obscurity is bordering on austerity at this point. As such, predominantly “thought leaders” in the tech sphere have taken it upon themselves to fill in the blanks with their own definitions.
The terms “AI Agents” and “Agentic AI” seem to be used so interchangeably that they mean the same thing. But there is a distinct difference between the two terms.
AI is much like a magician pulling a rabbit out of a hat for many ordinary, non-technical laymen.
Type in a prompt, and the AI automatically spits out a resemblance to the image that you had in your mind. This is called “Generative AI”.
Generative AI has put the power to create unique software into the hands of business users, producing a level of productivity that remains unparalleled. But generative AI’s weakness is that it needs human input to produce an outcome.
But doesn’t the term “AI” already imply that there exists a certain level of autonomy to begin with? Intelligence, albeit artificial, is to have free will; therefore, it is agentic by nature. Can’t AI figure out how to come up with the things that need to be done by itself?
The problem with setting up the agentic workflow for an AI agent to function correctly is that it requires a rock-solid framework. An AI agent does not operate on its own.
One loose end, and either the workflow is interrupted entirely or the functionalities are executed. This means that every step of its execution plan needs to be carefully engineered by someone with the right know-how.
LLMs are the technological foundation that makes AI agents understand context and human language. Agentic AI is the umbrella term. It would sit above the agents, the artificial foreman that orchestrates their collaboration and enforces compliance automatically.
AI Agents are agentic; they display agency over themselves and display the conversational capabilities of an LLM. One can argue that the term “Agentic AI” refers to the system’s autonomy as a whole, whereas an AI agent is merely an autonomous part of that system.
AI agents are a game-changer for businesses, as they can take initiative and operate independently of human input. Their popularity has exploded since we’ve discovered how to use AI to improve business processes and outcomes. These systems are able to operate “intelligently” on their own accord, as if they were sentient.
That being said, without human oversight, agentic AI is unpredictable and borderline harmful. Not in the “taking-over-the-world” sense, but poor implementation can lead to serious data leaks, jumbled system entries, and cause heaps of manual rework down the line. Proper know-how and guardrails need to be in place before businesses venture into agentic AI territory.
With Betty Blocks, you always maintain agency over AI automations, since all outcomes are confined to the platform itself. No accidental repository deployments, no whoopsie-daisies with (customer) data; that’s the guarantee of using AI-assisted software development on a low-code platform.
The platform has its own intuitive AI capabilities that fall into the Agentic AI category. The built-in co-builder is a form of our own AI Agent that helps our customers bring their business projects to life within seconds. Here's what generating an application with Betty Genius AI looks like:
Safety and security are observed at every step of the development process, even when AI or vibe coding is involved, thanks to the inherent guardrails of a low-code platform.
As for agentic AI, you can leverage external AI models directly from your Betty Blocks applications and create a wrapper to stylize them, as is standard for AI agents in other ecosystems. Subsequently, action workflows can be used to send and receive information between two systems, which can be modified and used to serve agentic AI purposes.
Knowing the difference between Agentic AI and AI Agents is becoming increasingly important in 2025 as enterprises start embedding AI deeper into workflows.
The terminology will keep evolving, but understanding the core concepts, such as reactive versus proactive, tool versus agent, will help you navigate whatever new buzzwords emerge.
The bottom line is, don't get too hung up on the labels. Focus on what these systems can actually do and how they fit your specific needs. The AI that solves your problem is the right AI, regardless of what category it falls into.