Agentic artificial intelligence (AI) is the next iteration of AI technology after generative tools like ChatGPT and Microsoft’s Copilot.
While GenAI can create unique content like text or code, it stops short of making autonomous decisions like installing that code on your computer. Agentic AI can also generate content, but it goes further than this to execute tasks and make decisions with limited or no human oversight.
This is promising stuff and it’s being hailed as the next frontier in AI technology with McKinsey noting that AI agents “have the potential to automate complex business processes—combining autonomy, planning, memory, and integration—to shift gen AI from a reactive tool to a proactive, goal-driven virtual collaborator.”
In this post we’ll define agentic AI, provide you with some tangible examples of how AI agents are currently being used, and break down what makes this technology work.
Agentic AI Explained in Detail
Agentic AI tools or “AI agents” are systems built using a combination of technologies designed to act autonomously, solve complex problems, learn from their environment, and adapt to new situations. The components of an agentic system include:
- Perception: The data gathering function that involves collecting and processing information from various sources like sensors, databases, user inputs, and IoT devices. Includes tasks like data filtering (removing noise or irrelevant data), transformation (converting data into usable formats), and feature extraction (identifying relevant patterns). The agent uses this information to perceive and understand its environment.
- Memory: This an agentic system’s knowledge base, the place where all that information it gathers is stored. It acts as the foundation for reasoning and learning, the repository it draws from to inform decisions and actions.
- Decision and Reason: This component is focused on analysis, reasoning, and inference. It allows AI agents to solve problems and make decisions. To do this, it various tools and processes including algorithms, rule-based symbolic systems, decision trees, and neural networks to draw conclusions and decide its next move.
- Action: The first three steps focused on information gathering, synthesis, and absorption. We humans call this “learning.” The next step for the agent is to apply the information to a specific real-world task. This could mean opening a browser window on your computer or guiding an autonomous car from point A to point B. We’ll get into some specific business examples in the next section.
- Adaptation: As with generative AI, agentic AI gets better as it ingests more data. It can learn from experience and adjust its actions based on new information. For example, that autonomous car we mentioned might modify its route from point A to point B based on real-time traffic activity for a given street or region.
How Is Agentic AI Different from Other AI Models?
The main difference between agentic AI and “traditional” or “narrow” AI systems lies in its autonomy and ability to perform complex, multi-step processes without human assistance.
While many AI systems are designed to perform specific tasks within predefined boundaries, agentic AI systems go a step further by making decisions, taking actions, and adapting to new situations.
Agentic systems are also self-reinforcing, meaning they can learn from their experiences and adjust their behavior as they process more data. While some generative AI systems (like large language models) can also improve with additional training data, they typically lack the autonomy and goal-oriented behavior that define agentic AI. Here’s a clearer breakdown of how agentic AI compares to other AI models:
1. Reactive AI vs. Agentic AI
Reactive or “rules-based” AI relies on pre-defined rules for its responses to stimuli. These systems are highly task-specific. They can’t adapt or learn beyond their programming. A conversional chatbot like ChatGPT can understand context, but it’s limited to conversational interactions and it requires user input to function.
Agentic AI can go beyond this by combining context understanding with autonomy, long-term memory, and proactive decision-making. This distinction is what sets agentic AI apart as a more advanced and independent system.
2. Tool-Based AI vs Agent-Based AI
Tool-based AI systems like Image classifiers are task-specific, static, and lack autonomy. They are excellent to use for very specific applications, but rely on user input and can’t act independently. ChatGPT and other conversational tools like Siri and Copilot are AI tools which stand alone in their functionality (e.g., to answer questions and provide information).
Again, agent-based AI is designed to operate independently, but it’s also adept at complexity. For example, agents can combine digital and real-world information to complete tasks in changing environments. Autonomous vehicles like Waymo are an example of this – a self-driving car must be able to adapt in real-time to its environment and make decisions based on constantly changing variables.
Here’s a quick checklist of the key characteristics of Agentic AI:
- Goal setting and pursuit: Goal-driven, autonomous, decision-making, adaptive. Agentic systems can define and prioritize their own goals based on the data they process, aligning their actions to achieve specific outcomes efficiently and effectively.
- Planning and acting independently: Agentic AI proactively creates detailed plans to achieve its objectives and executes those plans autonomously, no human involvement needed.
- Ability to learn and self-improve: By analyzing the outcomes of their actions, agentic AI systems can refine their strategies, improve their performance, and adapt.
- Acting in complex, dynamic environments: These systems are capable of operating in unpredictable and ever-changing settings. This makes them incredibly effective at managing supply chains, responding to real-time market fluctuations, and addressing customer service issues.
Real-World Examples and Use Cases
We’re still at the very early stages of agentic AI, particularly from a real-world use case perspective, but here are some examples that lend tangibility to this technology.
1. Agents for Data Pipeline Management
Agentic AI systems like Monte Carlo autonomously monitor data pipelines, identify issues (e.g., missing data or schema drift), and repair them without human intervention. This is an example of how agents perform multi-step task execution to achieve a goal.
2. Agents in Healthcare Diagnostics
AI agents developed by NVIDIA and GE Healthcare analyze medical imaging, such as X-rays and ultrasounds, to assist in diagnostics. These systems integrate with physical devices to provide real-time insights and improve patient care.
3. Agents for Workflow Automation
Tools like CrewAI and AutoGen automate multi-step workflows, such as content creation or data processing. They plan, execute, and optimize tasks independently, and make it possible for non-technical users to deploy complex automations.
4. Agents in Autonomous Vehicles
Waymo’s autonomous cars use agentic AI to navigate roads, adapt to traffic conditions, and make real-time decisions. These systems combine synthetic and real-world data to improve safety and efficiency in dynamic environments.
Ethical Considerations and Risks
Agentic AI systems have incredible potential. According to Gartner, by 2028, 33% of enterprise software applications will incorporate AI agents in some way, up from 1% in 2024. But these tools also pose some specific ethical challenges and risks that must be addressed to ensure their safe and responsible use. Key concerns include:
1. Unpredictability
Agentic AI systems, especially those with high levels of autonomy, can be unpredictable. Their ability to adapt in the moment makes them effective, but it can also make them take actions that deviate from human expectations in disastrous ways. A Tesla turning onto the tracks towards an oncoming train while in self-driving mode is an example of how wrong things can go.
2. Value Alignment
Humans and robots may seem like they’re on the same page, but we operate quite differently under the hood. AI systems are designed to complete tasks and they will work towards achieving this goal in ways that are efficient, though not necessarily ethical. Example: misaligned systems may prioritize efficiency or profitability over fairness, safety, or societal well-being. An AI system optimizing supply chains might inadvertently exploit labor or environmental resources. Researchers are actively exploring ways to embed ethical frameworks into AI systems to address issues like this.
3. Transparency and Control
As agentic AI systems become more autonomous, clear frameworks around who is responsible for the decisions these systems make—and how they’re made—must be established. Lack of transparency can erode trust and make it difficult to hold systems accountable. More importantly, it can cause real harm from systems that may drive your car into an oncoming train or misdiagnose your illness or tank your stock portfolio. Oversight and regulations are needed to protect the public and hold companies that design these tools accountable.
The Future of Agentic AI
As the use of agentic AI continues to expand and proliferate, it will become more sophisticated and complex. Agents are moving us toward the realm of artificial general intelligence (AGI), a term used to describe systems with human-level intelligence that can learn, adapt, and apply knowledge in any situation, domain or environment.
This is the stuff of science fiction novels, but we’re not there yet. Agentic systems have opened the door a bit wider. They’re already demonstrating tremendous potential in business, research, healthcare, and everyday applications.
From a pure practicality perspective, next-gen digital assistants are already incorporating AI agents to handle complex, multi-step tasks autonomously in areas like supply chain management and finance. Companies are agents them to execute tasks, but they’re also finding, according to a new CIO survey, that agentic tools require more in-depth integration then standalone genAI chatbots.
It’s a shift that’s bringing us closer to the vision of AGI, where systems can operate with human-like intelligence across diverse domains.
Harness the Power of Intelligent Decision-Making—Bring Agentic AI into Your Personalization Strategy
Agentic AI represents a transformative leap in technology. It appears anything is possible when systems can autonomously make decisions and adapt to issues in the moment, just like a human worker might.
These tools can achieve complex goals with minimal human intervention and that makes them valuable to companies in a plethora of ways, including streamlining customer interactions and experiences.
Automated personalization gets you a step closer to using agentic systems by helping to streamline and unify your data. Platforms like Monetate also apply machine learning and AI to adapt to real-time customer behavior, customizing user experiences across digital channels.
Explore Monetate’s guide on automated personalization and learn more about how machine learning helps AI systems make better decisions over time.