What Is Agent Function in AI: A Technical Overview

While many experts discuss the future of artificial intelligence and its potential impact on the world and society, we can safely assume that there is a future of some kind. The whole AI market may turn out to be a massive bubble, but, just like in the case of many other bubbles, it will inevitably leave some great products that will define how we interact with a wide range of products.

An interesting topic to discuss is the agent function in AI at large and various applications of agentic artificial intelligence in contemporary products and services. If you are not immersed in the heated debate around the use and development of AI, the distinction between agentic AI and other forms of artificial intelligence may feel obscure and inconsequential. However, there is a difference.

Modern autonomous systems in technology

When it comes to the idea of autonomy, any system that can operate without human intervention can be considered somewhat intelligent. For instance, many automated switches used in electrical grids are mechanical in nature yet can execute a function under certain conditions without any additional instructions from an overseer.

Some of the simplest automated products, appliances, and systems have a certain level of autonomy. Artificial intelligence often refers to something that can operate on a higher level. Expert systems can process vast swaths of data and identify patterns with an incredible level of consistency. For instance, some of the most advanced neural networks reliably outperform trained doctors when it comes to cancer identification on MRI scans.

Many contemporary systems use all sorts of approaches to arrive at a level where they can function well. Reinforcement learning algorithms have been making the most progress during the last decade. The method was popularized mostly by the achievements of OpenAI’s ChatGPT, which relies heavily on this particular way of incentivizing a system to learn.

All of the above is about the general idea of artificial intelligence. Agentic AI is somewhat different. It can be defined as follows:

1. An agent is capable of independent thought, reasoning, and data processing.
2. An agent defines steps necessary to achieve goals determined by users.
3. It can create intermediary tasks that must be accomplished to achieve said goals.
4. It takes these steps and executes all the necessary processes to complete them.

In some sense, agentic means that a system has an agenda and acts without any meaningful human intervention outside of receiving its initial goals.

AI decision-making processes

Agents must have the necessary tools and approaches in their arsenals to define and solve tasks while trying to achieve user-defined goals. It means that an AI must reason using some form of decision-making process.

Most methods involve:

1. Data input phases when AI agents process structured and unstructured data.
2. Pattern recognition is the phase where NLP and neural networks identify correlations within data.
3. Prediction and optimization are the necessary stages to generate reliable outputs.
4. The feedback loop is the phase where reinforcement and other methods are used to refine outputs.

While many models exist, recent research and development efforts focused primarily on several most successful ones:

• Rule-based decision-making is a good way to build systems that must identify certain categories of data. For instance, spam filters are using this approach by labeling certain types of letters as scams or newsletters.
• Pattern discovery is often categorized as unsupervised learning, where data is unlabeled and the system must do the labeling autonomously. A good example is the recommendation engine that Netflix uses to make your homescreen engaging!
• Reinforcement learning is all about improving the feedback loop by rewarding or penalizing a system that makes decisions. OpenAI’s ChatGPT and DeepMind’s AlphaFold use these methods to improve outputs. The latter is responsible for many breakthroughs in protein structure prediction.
• Deep Learning is a method that employs a wide range of data sets (images, voice recordings, textual information, etc.) to identify patterns. Such systems are used to train a variety of complex AI agents like Tesla’s Autopilot.

It is a good idea to crunch some numbers in the field of AI decision-making. Here are some interesting statistics:

• IBM Watson is one of the leading systems in the healthcare sector. It can analyze over 10 thousand medical documents in a single day to provide suggestions on treatment, diagnoses, and care.
• JPMorgan and other investment banks are saving close to $150 million by using AI for fraud detection, eliminating 95% of fraud attempts.
• Amazon uses its agent to make decisions about warehouse stocking and cut associated costs by over 20%, according to company reports published in 2024.

When it comes to decision-making by autonomous agents, ethical AI challenges take the stage. It is hard to identify the best course of action to evaluate decisions, improve outputs, and regulate them.

Note that many companies are pushing AI-driven decision-making into sensitive areas like healthcare, education, social policy, and more. It is crucial to make sure that we can understand, manage, and improve these processes.

Real-world AI applications

Agentic AI can be used across many industries and products, as proven by already effective systems that you can test personally.

Here are some interesting examples:

• Shopify and Clari use artificial intelligence to identify the best sales strategies and reduce response times by at least 90%.
• Eve by Insilico Medicine cuts research times by 70% due to its ability to discover potential drugs and analyze results of experiments.
• Renaissance Technologies uses agents to increase annual returns from proactive trading by 66%, according to Bloomberg.
• GitHub Copilot is already very helpful, as it writes close to 46% of the code published on the platform by developers.

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The future of AI agents

Regulatory frameworks are still somewhat underbaked and require a lot of additional discussions, adjustments, and honing. The EU AI Act came into force back in August 2024. While it addresses some concerns across all possible applications, the methods of risk identification and some exemptions leave a lot of room for interpretation and troubling gray areas.

A big issue for many is the carbon footprint of massive data centers used to train huge AI models. The race to the first AGI is fast-paced. According to Forbes, a single large language model generates over 600 thousand pounds of CO2, which is more than 125 round-trips on a jet between New York and Beijing.

On the other hand, you don’t have to worry about many of these concerns, as they will likely sort themselves out within the next decade. A much more important question is how you can capitalize on the growing AI trend!

The DeFi sector is already embracing the hype surrounding artificial intelligence, with many protocols pivoting to implement machine learning or focusing on AI agents. Navigating the DeFi ecosystem is a challenge even for experienced veterans. Newcomers are often overwhelmed with the variety of investment options.

Crypto enthusiasts who want to explore the DeFi sector should work with platforms that provide useful services. Rivo has a great Yield Marketplace, where you can browse a variety of investment strategies picked by experts and verified by trained specialists. It is a great destination for investors seeking better ways to build balanced crypto portfolios!