Generative AI can write texts, generate images, and spit out code. Sounds good, but what if you want the AI not just to respond, but to act independently? That’s where Agentic-AI comes into play: AI systems that autonomously perceive, infer, and execute actions to achieve an overarching goal. With minimal human oversight.
And no, there really isn’t a unified definition. Even MIT Sloan notes in 2025: “There isn’t a universally agreed upon definition of agentic AI.” So, the concept is still evolving.
Generative AI vs. Agentic-AI
The difference is simple: Generative AI creates content based on your input. Agentic-AI takes this ability and goes a crucial step further by executing actions in real systems. Google Cloud describes Agentic-AI as a subset of generative AI, where LLMs serve as the “brain” that orchestrates and controls agents.
Imagine asking ChatGPT: “Write me an email.” Generative AI provides the text. Agentic-AI would write the email, pull the appropriate recipient from your CRM, send the email, and update the status in the project management tool. So not just responding, but truly acting.
The Core Characteristics
What makes an AI system “agentic”? Five characteristics repeatedly appear in the literature:
- Autonomy: Completing tasks without constant human intervention
- Proactivity: Anticipating needs instead of just reacting to prompts
- Adaptability, meaning learning from experiences and adjusting behavior
- Specialization: Multiple hyperspecialized agents working together
- Agents communicate with each other and with humans (collaboration)
The typical work cycle goes like this: Perception, inference, planning, action, reflection. And then it starts over. This iterative loop is what distinguishes Agentic-AI from a simple chatbot.
Where is it Already Used?
According to a survey by MIT Sloan and Boston Consulting Group, by 2023, 35% of respondents were already using AI agents, with another 44% planning to start soon. Customer service, supply chain optimization, fraud detection in finance, support in medical diagnoses, the application areas are quite broad.
At Exord, we heavily rely on Agentic-AI and Multi-Agent Systems, where multiple specialized agents work together. The possibilities are real, but they require a clean architecture.
But: Not Everything is Rosy
The collective understanding of the societal impacts of Agentic-AI is, according to MIT-Sloan Professor Sinan Aral, “nascent, if not nonexistent.” Frankly, data quality, governance, and security remain central challenges. And the risk is real that organizations will adopt this technology without a formal strategy, simply because the hype is so great.
The Financial Times compares the current state to autonomous vehicles: Most applications are at Level 2 or 3. Full autonomy (Level 5)? Remains theoretical.
And Now?
Agentic-AI is not a buzzword that will disappear next week. Nvidia CEO Jensen Huang spoke at CES 2025 of a “multi-billion-dollar opportunity.” Whether this will come true remains to be seen. What is certain, however, is that the technology is fundamentally changing how we think about AI applications.
If you want to know which services in this area make sense for your company, stop by or contact us directly. We’ll take a look at it together.
