Artificial intelligence continues to evolve at breakneck speed. More powerful and intelligent models and technologies are launching regularly. Among these, agentic AI has attracted particular attention as one of the most innovative and widely discussed types. In this article, we'll break down what it is, how it works, and what sets it apart from traditional AI. You'll discover where it's already being deployed and the key challenges that come with these systems.
What Is Agentic AI
Before delving into the details, let's look at the agentic AI definition. Agentic AI refers to advanced systems that can tackle multi-step tasks and make autonomous decisions through iterative planning and complex reasoning.
Modern agent-based (agentic) AI systems are often built on top of large language models (LLMs) and other machine learning components, but they offer much broader functionality. They are capable of acting autonomously, making decisions, and adapting to changing conditions.
There are many platforms of this type available today. Each has its own unique features and operating mechanisms. To better understand their principles, let's look at how agentic AI works.
These systems follow a series of key stages when solving problems:
- Data collection. Agentic AI gathers information from diverse sources — sensors, APIs, databases, and interactions with humans or other AI systems. As it does so, it identifies objects and extracts features for analysis.
- Reasoning. The system processes information using tools like computer vision and NLP. This allows them to accurately understand the problem, identify the best solution, and choose the optimal actions to implement it.
- Goal setting. The AI determines the objectives of the task assigned to it based on user requests or the predefined goals of a specific model. It then formulates a strategy for achieving these objectives based on reinforcement learning, decision trees, or other algorithms.
- Decision-making. After setting goals and developing a strategy, artificial intelligence compares possible courses of action and selects the best one. To achieve this, it can use reasoning, utility functions, or probabilistic models.
- Execution. After identifying and making the optimal decision, the AI takes action to achieve the desired outcome. In doing so, it often interacts with users, external systems, or equipment via APIs or other interfaces.
- Learning and adaptation. After completing a task, the agent-based AI evaluates the results and collects feedback to optimize further processes. By using self-learning or reinforcement learning, it continuously improves its capabilities and can solve similar problems faster and more efficiently in the future.
How Agentic AI Differs From Traditional AI
Understanding these distinctions helps explain why agentic AI is increasingly viewed as the next evolutionary leap in artificial intelligence. Let's examine the key differences between autonomous AI systems, generative AI, and AI agents, defining their functionality, capabilities, and applications.
Agentic AI vs. Generative AI
Although agentic AI is a modernized version of generative AI models, there are several important distinctions between them. Generative LLMs operate exclusively within the patterns they are trained to follow, and they function in a highly formulaic manner. They can perform a wide range of actions, including content creation, data analysis, and other similar tasks. However, to do so, they require specific, precise, and unambiguous instructions from the user.
Agent-based AI extends the capabilities of generative AI models by carrying out tasks autonomously and proactively. Given a basic set of instructions, it performs multi-step actions, applies reasoning, and makes decisions with incomplete or ambiguous input data.
For example, generative AI can retrieve current airfare prices for the dates and destinations you're interested in. Agent-based AI can also create the most time- and price-optimal flight routes and automatically book or purchase tickets.
Generative AI operates strictly according to a predetermined algorithm — it responds to input and produces output. Agentic AI has greater freedom of action, autonomously and proactively executing tasks to solve complex problems.
Agentic AI vs. AI Agents
A broad term describing an entire system (framework) of interconnected AI algorithms. AI agents are individual components or models within this framework, performing individual or collective tasks to achieve a given goal. Several AI agents, combined into an interconnected and coordinated system, become agentic AI.
Systems controlled by artificial intelligence, such as smart homes, can incorporate agentic AI elements. The algorithms that control individual smart home functions (lighting, heating, security, and others) are called AI agents. Each agent performs a specific function, but together they operate within the agent-based AI framework to achieve a shared goal.
Real-World Applications of Agentic AI
Agentic AI is already making waves across numerous industries, with adoption accelerating rapidly. Autonomous and proactive algorithms are being applied in business, science, industry, and everyday life.
The most common and high-demand areas where agentic AI is already demonstrating significant advantages include:
- Customer service. AI agents improve customer service by quickly responding to user requests and finding effective solutions to their problems. They can also provide support to operators and automate many routine tasks.
- Content creation. Agent-based AI effectively streamlines the creation of various media content formats. It delegates specific tasks to specialized generative AI models (text writing, image and video generation, and so on). The result is more complex, personalized, and relevant products.
- Scientific and industry research. Agent-based artificial intelligence is capable of conducting research in a number of fields and industries at a professional level. It often identifies potential new materials based on combinations or transformations of existing ones.
- Healthcare. Agentic AI frameworks process large volumes of medical data, extracting key information and supporting clinicians in diagnosis and treatment planning. They can also consult patients and doctors 24/7, providing consistent, personalized guidance to their queries.
- Manufacturing. The ability of AI agents to make complex decisions and perform multi-step operations that rely on external systems is highly valued in many industries. They can significantly automate the entire production cycle — from sourcing materials to delivering finished goods.
- Security and risk management. Agent-based AI has proven itself in managing security systems and risk-mitigation processes. It proactively monitors threats, anomalies, and suspicious activities, analyzes these patterns, and quickly makes appropriate decisions.
- Information systems. AI agents support developers and users of knowledge bases and other information systems. With access to the databases, AI agents can quickly find relevant data upon request, as well as structure and analyze it to generate valuable insights.
Challenges, Risks, and Ethical Concerns
The development of intelligent agents opens up exciting opportunities and raises new challenges, risks, and ethical questions. The large-scale autonomy and proactivity of agentic AI intensify these challenges, making privacy, data quality, reliability, and accountability highly important. Each of these challenges requires careful consideration. This consideration will determine how securely and reliably these systems will be integrated into everyday life.
Privacy and Security
Agent-based AI can pose heightened security and data privacy risks compared to generative AI. The autonomy and proactivity of AI agents give them wide operational freedom, making them extremely attractive to cybercriminals and other malicious actors.
Containerization and anonymization of personal data handled by AI systems will help address this challenge. Companies should divide AI agents into several categories (for staff support, customer service, and similar functions) and grant them different levels of access to data.
Reliability and Predictability
Generative AI and other early artificial intelligence models typically rely on clear, predefined instructions to operate. This makes their output more or less predictable and controllable.
Agentic AI operates with a high degree of autonomy, making decisions and planning its actions independently. This increases its efficiency and provides a number of other advantages. However, it also introduces some unpredictability into both the process and the outcomes of AI agents’ actions.
Data Quality and Relevance
The accuracy, performance, and other qualities of agentic AI tools directly depend on the currency and relevance of the data they rely on. Lack of reliable access to high-quality data reduces the potential of AI models and can lead to user-facing issues due to errors or model inaccuracies.
Data streaming platforms (DSPs) can help address this limitation. They can collect, process, validate, and seamlessly deliver data from multiple sources in real time.
Ethical Issues
The emergence and development of AI agents raise not only security risks and data quality issues but also ethical concerns. The autonomy and freedom of action of such algorithms encourage humans to delegate excessive responsibility to them, including during critical processes. The consequences of this trend could, at a certain point, become difficult to reverse and potentially harmful.
High levels of autonomy in agent-based AI must be combined with adequate means of control. Continuous monitoring and evaluation of its performance are essential, as is ensuring complete transparency in interactions between AI and humans.
Why Agentic AI Matters
Agent-based artificial intelligence is increasingly being called the future of AI. This is primarily due to its ability to act as a bridge between static, template-based programs and the dynamic, highly adaptive intelligence of humans.
AI agents are the first generation of autonomous programs capable of acting with a high degree of autonomy and proactivity. They reason logically, perform complex multi-step actions, and find innovative solutions to problems and challenges. This makes their behavior appear more human-like in certain tasks, though they do not operate like the human brain.
The ability of agent-based AI to operate effectively without constant user supervision or control, or with minimal user input, is a key advantage of such systems. This autonomy has become a significant breakthrough in the development of artificial intelligence.
The trend toward agentic AI is significantly expanding the potential for AI agents to be used across a wide range of industries and sectors. In the near future, they could evolve into a more advanced form of artificial intelligence. Such systems may not only follow human instructions but also orchestrate other AI models, software systems, and various programs.