Intro

AI agents. Agentic workflows. Autonomous agents. Intelligent agents. Digital agents. Task-oriented agents. Smart agents. Copilots. AI personas. AI assistants. Embodied agents etc.

The topic of agents is so hot right now that no one even knows what the correct term for them is. What's more, in machine learning, the concept of agents originally meant something different than what it means in AI today. In this series on agentic systems and workflows, we will clarify these terms and set the record straight. There is indeed a lot of confusion and questions surrounding this topic. From theoretical frameworks to practical applications, current innovations to future potential, policies and roadmaps, we will cover it all.

We don’t know if AGI is at the end of this path but we will be open to it. And to emphasize that we will start our series … with open-endedness ;) That was one of the topics suggested by our readers here. 

In today’s episode:

Why open-endedness is important in the context of AI agents?

To put it simply, open-ended systems do not have defined boundaries, which means they have the potential to generate novel ideas and solutions beyond what was originally programmed or anticipated. This lack of fixed constraints allows for continuous exploration, discovery, and even the evolution of strategies and behaviors within the system. In AI, open-endedness can be crucial for pushing the limits of creativity, innovation, and problem-solving – traits that are increasingly valuable as AI systems tackle more complex, real-world challenges.