Over the last few years, we’ve seen significant growth in Agentic AI, which refers to intelligent systems that can operate on their own while engaging in conversations. This evolution began with advances in Generative AI, particularly with Large Language Models (LLMs).
Here’s a quick breakdown of the key steps in this journey:
- Starting Point – Large Language Models:
– The first step involved creating LLMs, which were trained to understand and generate text. These models formed the basis for future intelligent agents.
- Enhanced Context Handling:
– As LLMs improved, they could handle larger pieces of text, allowing them to understand and respond to more complex documents effectively.
- Using Tools and Real-Time Information:
– The introduction of Retrieval-Augmented Generation (RAG) allowed these models to access up-to-date information, improving their responses by integrating various tools, like search APIs.
- Multi-Modal Capabilities:
– Next, these systems evolved to handle different types of input (like text, images, etc.) and produce varied outputs, laying the groundwork for more advanced agentic systems that could perform complex tasks with less human guidance.
- Current AI Agent Architecture:
– Today’s AI agents are more sophisticated. They have:
– Memory systems that help them remember past interactions.
– The ability to use third-party tools for tasks such as booking flights or searching for information.
– A decision-making process that allows them to try different approaches if the first one doesn’t work.
– Enhanced knowledge bases that help them make better connections and reason more effectively.
- Looking Ahead:
– The future of AI agents is promising, with ongoing developments that will enhance their capabilities even further.
For more detailed insights into these advancements, you can check out the link provided to participate in our online/onsite conference: The AI Day on the 8th of May 2025 or/and The Online/Onsite Chief Artificial Officer Certification on the 2-4th May 2025.