What Data Scientists Need to Know About AI Agents and Autonomous Systems

AI agents have been a subject of active research across the Artificial Intelligence (AI) scientific community for several decades. An AI agent is a software entity capable of perceiving its environment, reasoning about it, and acting autonomously, making them suitable for automating arduous tasks requiring constant decision-making, actions, and sometimes communication with other AI agents.

Are AI agents and autonomous systems the same? It depends on what we mean by “autonomous”: some AI agents can be considered autonomous systems, such as those that operate on their own within predefined settings, while others may require regular human intervention.

In recent years, we have also started to hear the term “agentic AI,” which refers to an evolved form of AI systems that exhibit greater autonomy and adaptability, meaning that they can tackle more complex tasks, continuously learn from experiences, and process large amounts of data to improve their performance.

Both autonomous AI agents and agentic AI systems are currently being applied in domains as diverse as advanced chatbots for customer support, navigation by autonomous vehicles, process automation in finance and logistics, and the videogame industry, where NPCs (non-playable characters) are increasingly infused with AI-driven behavior.

There is one thing all these applications have in common: there are data—tons of data to analyze and learn from. As part of bridging the gap between data science processes and the application of AI agents and autonomous systems, this raises an interesting question. What should a data scientist know about these branches of AI, and how can they leverage them into better data science-based solutions?

Key Concepts and Topics Related to AI Agents and Autonomous Systems

 
In the remainder of this discussion, we will list five essential knowledge subareas data scientists should put their lenses on about AI agents and autonomous systems:

1. Agent architectures and decision-making

Multiple frameworks exist for designing AI agents, such as reactive, deliberative, and hybrid architectures. Likewise, it is important to understand agent-driven decision-making processes, including common steps like automated planning, reasoning, and goal-directed behavior. Data scientists who get familiar with this knowledge will be in a better position to envision intelligent agents that can autonomously apply data science processes, namely process data, make data-driven decisions, and act, reducing human intervention and boosting efficiency in data-driven scenarios.

2. Multi-agent systems and communication

What could be more intriguing than having a single AI agent analyzing data and acting on its own? Having multiple agents interact, collaborate, and negotiate in shared environments, of course! What data scientists should learn about in this domain is primarily related to well-established protocols for inter-agent communication and distributed problem-solving. Multi-agent systems have demonstrated significance in tackling large-scale, distributed data science problems, for instance, logistics network optimization, improved recommender systems, and smart city solutions that are “smarter”.

3. Reinforcement learning in autonomous systems

Reinforcement learning is considered by many a subarea of machine learning that investigates heavily algorithmic systems that learn by themselves from experience (trial and error). Not an unknown area by some data scientists, key aspects to familiarize with are the distinct types of reinforcement learning algorithms whereby agent-based systems learn to optimize (sequences of) actions based on rewards. Reinforcement learning algorithms are widely used in applications like navigation, robotics, and game AI, and knowing them enables data scientists to develop systems that dynamically improve from feedback, being ideal for real-time data science problems like predictive maintenance and adaptive pricing models.

4. Environment modeling and simulation

before deploying agent-based AI systems in the real world, it is usually necessary to test their behavior in controlled environments. This raises the need for data scientists to learn to build and use simulations for testing AI agents, as well as explore tools for creating virtual environments like digital twins that mirror real-world complexities. In essence, they’ll learn the nuances of prototyping and testing models in risk-free settings, ensuring robust performance before deploying systems in unpredictable real-world settings.

5. Adaptation and lifelong learning

As data keeps being continuously collected and might constantly evolve in most applications, data scientists should understand how AI agents can learn from new data and experiences over time and adapt their behavior without being necessarily retrained from scratch. Techniques for online learning, transfer learning, and self-improving autonomous systems are essential to empower data science solutions and keep them relevant and effective as data evolves, especially in areas like customer personalization, fraud detection, and medical diagnostics.

Wrapping Up

 
This article discussed several concepts and topics within the realm of AI agents and autonomous systems that data scientists are highly recommended to learn as part of their continuous journey to keep up with constant advances in the AI field. As AI agents, autonomous systems, and their latest “evolved version” agentic AI, become part of the latest data-driven systems like generative AI systems and language models, pushing their boundaries, it is important for data scientists to become familiar with these other interrelated branches and are also part of the huge tree of AI.
 
 

Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.