Welcome to the era of agentic AI, where smart systems aren't just following orders but actively solving problems like clever colleagues. What is agentic AI? Agentic AI refers to an artificial intelligence system that  can analyze data, develop solutions and put them into effect autonomously.

‍

At Rellify, we're leveraging agentic AI workflows for content opportunity analysis so you can spot and speak to the relevant topics and trends ahead of your competition. Our patent-pending platform automatically creates custom AI models trained on relevant market data to be a subject matter expert. Our platform can then autonomously select the right keywords, phrases and natural language queries associated with topics related to the growth opportunities you decide to act on.

‍

We'll be happy to show you how it works. Just contact us for a free demo.

‍

Understanding this shift toward autonomous decision making and execution is like discovering electricity while competitors still use candles. Another remarkable AI evolution  has occurred — from digital assistant to digital strategist.

‍

In this article, we'll explore what's happening now and what's waiting just around the corner. We'll look at specific examples of how these intelligent agents are bringing innovation to fields like healthcare and manufacturing. And we'll examine the possibilities alongside the challenges. Then you'll be set to leverage agentic AI for both competitive advantage and lasting growth.

Types of agentic AI systems: A comprehensive overview

Agentic AI represents a leap in artificial intelligence: autonomous systems that don't merely follow instructions but independently perceive, decide, and act with minimal human oversight. At their heart, these systems use advanced algorithms to learn from and adapt to their environments.

‍

One example that dramatically brings this home is the way two AI agents can now talk to each other in "Gibberlink Mode," a protocol tailored for machine efficiency that enables AI-to-AI interactions. The fact that they communicate in a language that humans can't understand, however, raises questions involving control, ethics, and security.

‍

Developers have created different types of agentic AI systems with specific capabilities and purposes in mind. Let's look at the major categories of these AI applications and systems and what makes each distinct.

Reactive agents

Reactive agents, the simplest form of agentic systems, operate based on the current state of their environment without maintaining memory of past states or actions.

‍

These systems follow a direct stimulus-response model, matching specific situations to specific actions. Think of them as implementing sophisticated "if-then" rules, like a chess-playing program that evaluates the current board state to select a move without explicitly planning multiple steps ahead through internal modeling.

Model-based agents

Model-based agents maintain an internal representation (or model) of their environment, allowing them to understand how their actions affect the world around them. This internal model enables them to predict potential outcomes before acting. These agents excel in complex, dynamic environments where understanding causal relationships is crucial — like controlling an autonomous vehicle.

Goal-based agents

Rather than simply responding to their environment, like model-based agents, goal-based agents continuously evaluate how different actions might help them achieve specific objectives. They can use means-end predictive analytics to consider actions will lead to desired outcomes.

Utility-based agents

Utility-based agents refine the goal-based approach by measuring the desirability of different states. Instead of viewing goals as binary (achieved or not), these systems can evaluate how preferable one outcome is compared to another. A financial trading agent, for example, would go beyond a goal of "make money." A sophisticated utility function could  balance potential returns against risk exposure, liquidity constraints, and diversification principles.

Learning agents

Learning agents incorporate mechanisms to improve their performance over time based on experience. They modify their internal models, decision processes, or evaluation criteria as they interact with their environment.

‍

A recommendation system is an example of this approach. Initially, it might make suggestions based on general patterns, but as it observes user interactions, it refines its understanding of individual preferences to provide increasingly personalized recommendations.

Multi-agent systems

In multi-agent systems, AI-powered autonomous agents interact within a shared environment. These agents may cooperate toward common goals, compete for resources, or engage in mixed strategies with both cooperative and competitive elements.

‍

Smart traffic management systems demonstrate this approach, with individual intersection controllers coordinating to optimize overall traffic flow while each manages its local conditions. Coordinated behavior of multiple agents works better than centralized control.

Hybrid architectures

In practice, many sophisticated agentic systems employ hybrid architectures that combine elements from multiple categories. These systems might use different approaches for different aspects of their operation or switch between modes based on context.

Real-world applications of agentic AI

Agentic AI isn't just changing industries — it's helping to reinvent them. Here are two examples of how these intelligent systems are being applied.

‍

GE Healthcare has leapt into agentic AI with cloud-related research and development efforts. Their "Health Companion" project explores whether an agentic AI approach driven by multiple agents, each an expert in a particular area (i.e., genomics, radiology, pathology), could help physicians streamline their clinical decision-making and deliver more personalized care. Their vision for the project is for these agents to collaborate and analyze multi-modal data in order to proactively generate treatment plan recommendations, continuously adapting based on new information.

‍

Toyota is one manufacturing company that's been raising operational efficiency with agentic AI. Its “O-Beya" system draws on design data from real-life engineers and other sources to inform nine specialized AI agents. If, for example, a car designer asked O-Beya how to make a car run better, an "engine agent" might give an answer related to engine output while a "regulatory agent" provides an answer on limits to emissions, which O-Beya would then consolidate into a single reply. 

What is agentic AI's future

We can expect agentic AI to become more integrated into business strategy, but ethical considerations and regulatory frameworks will be critical for its responsible development and scalability. Here are just a few things you can anticipate in the development of agentic AI:

‍

• Increased automation. AI will handle more complex, real-time tasks across sectors like marketing, healthcare, finance, risk management, and autonomous driving.
• Personalization & interactivity. Expect more adaptive, human-like AI that personalizes interactions in areas like virtual assistants, e-commerce, and education. AI will increasingly understand and respond to human emotions, making interactions more natural and useful.
• AI ethics and governance. As AI autonomy grows, there will be a stronger push for ethical guidelines, regulation, and making sure AI aligns with human values to avoid harmful outcomes. AI content generators themselves have already run into some ethical snags in education, as students use AI generators to complete assignments.

Rellify's AI difference

Rellify will continue evolving alongside agentic AI technology, harnessing it to help businesses find topics and create content that resonates with their target customers.

‍

As we have seen, part of agentic AI involves one model thinking about how to solve a problem and then asking other models to go solve it. In a sense, that's what a Relliverse™ does for our clients. It "thinks" about the content opportunities and then prompts another model to capture them. ‍With a custom Relliverse™, you can leverage deep learning, machine learning and AI research to change the way you plan, write, and distribute content.

‍

Ready to find out how? Contact a Rellify expert today for a demo and find out exactly what you're missing.