What is Agentic AI?
Artificial Intelligence
At the core of intelligent apps lies artificial intelligence. AI itself is not new—technologies like machine learning, decision systems, and robotics have been evolving for years. In the last couple of years, Gen AI has burst onto the scene, unlocking entirely new possibilities and redefining what’s possible.
Two terms frequently come up in discussions: AI Agents and Agentic AI. They sound similar but refer to distinct concepts. So let's break it down.
AI Agents
An AI agent is a system that performs specific tasks, reacting to inputs and following rules.
An example could be a customer service chatbot that handles routine queries, such as informing customers of their account balance or processing payments. These agents follow predefined scripts, provide answers, complete tasks, and reduce the need for human intervention, making them efficient at managing repetitive, simple tasks.
Agentic AI
Agentic AI goes beyond this. Instead of just following fixed rules, it can think, adapt, and make independent decisions. It actively learns, adjusts its approach, and develops strategies to solve complex problems with minimal human involvement.
In scientific research, for example, an agentic AI system could analyze vast datasets, develop its own hypotheses, and run experiments to refine its understanding. A real-world use case is in pharmaceuticals, where companies use agentic AI for drug discovery through molecular simulation, rapid screening, and analysis—achieving levels of speed and accuracy that were once considered impossible.
This makes Agentic AI much more powerful, capable of solving difficult problems and driving innovation.
Multi-Agent Systems
Another key term is multi-agent systems, which are collections of individual AI agents working together collaboratively to solve complex problems.
This is similar to microservices-based architectures, where a complex problem is broken down into smaller units of work. This modular design allows agents to specialize in specific tasks, improving efficiency and scalability, but it also introduces challenges in coordination and communication between agents.
Smart Building
Consider an example in a smart building. A single AI Agent could be a smart temperature control system that turns the heating on or off to maintain a specific range.
A multi-agent system could involve multiple instances of temperature control working alongside other agents managing air quality, security, lighting, elevator maintenance, water management, and occupancy detection. Together, they optimize the building’s performance.
An Agentic AI system in this scenario wouldn’t just follow preset rules—it would learn from historical data, spot patterns, and adapt in real time to optimize energy use, air quality, and the carbon footprint while keeping occupants comfortable. Over time, it would make predictions and adjust proactively before problems arise.
Evolving
Agentic AI is still evolving. The first wave of agents, which are common today, perform tasks such as retrieving information, grounding themselves in data, reasoning, summarizing, answering questions, generating content, and taking actions when asked.
The second wave of autonomous agents represents the new frontier, emerging in response to customer needs and shaping the future of AI-driven applications.