Agentic artificial intelligence is now at the forefront of technologies used to optimize business processes, automate complex tasks and make strategic decisions. While many organizations are exploring the potential of this advanced AI technology, agentic AI systems are being integrated into more and more business processes every day. So what exactly is this technology that is rapidly transforming the digital world and how does it work?

What is Agentic AI?

Agent-based artificial intelligence refers to artificial intelligence systems that can make decisions on their own, act autonomously for specific purposes, and interact with their environment. These systems are complex software that actively works to achieve its goals, is capable of planning and changing strategies according to the situation, unlike passive algorithms that execute only given commands.

The word agentic comes from the English concept of “agency” and refers to the capacity of an entity to act on its own volition, to influence its environment. In the context of artificial intelligence, on the other hand, it defines the ability of the system to plan and implement on its own the steps necessary to carry out the given tasks, at a certain level of autonomy.

According to Forrester's “The Rise of AI Agents” report in 2023, the use of agentic AI systems in the global business world has increased 187% in the past two years, and this growth is expected to continue gaining momentum over the next five years.

Key Features of Agentic Artificial Intelligence

There are a number of key features that distinguish Agentic AI systems from other AI applications:

Autonomous Decision Making

Agentic artificial intelligence systems are capable of making decisions on their own without human intervention. These systems analyze the data they collect from the environment, assess the current situation and select the most appropriate action to achieve their goals. For example, the agentic artificial intelligence system used in a financial institution can analyze market data to make investment decisions or detect potential risks.

Goal-Oriented Behavior

These systems act for specific purposes. They plan and implement the necessary steps to achieve the goals for which they are programmed. They know how to look for alternative paths and change strategies when there are obstacles to achieving their goals.

Interaction with the Environment

Agentic artificial intelligence is constantly interacting with its environment. It detects changes in its environment through sensors, APIs, databases, and other information sources and reacts appropriately to those changes. Feedback from the environment plays an important role in shaping the behavior of the system.

Learning and Adaptation

Modern agentic artificial intelligence systems can learn from their experiences and improve their performance over time thanks to machine learning techniques. With methods such as reinforcement learning, they can optimize their strategies by understanding which actions produce better results.

According to the study “The State of AI Agents,” published by MIT Technology Review, agentic AI systems with learning and adaptation capabilities perform 43% more than static systems.

Differences Between Agentic Artificial Intelligence and Traditional Artificial Intelligence

There are important differences between agentic AI and more traditional AI approaches:

1. Proactive vs. Reactive Approach: Traditional AI systems are often reactive, meaning they respond to it when they receive a certain input. Agentic AI, on the other hand, is proactive; it can act spontaneously to achieve its goals.
2. Autonomy Level: Agentic artificial intelligence has a higher level of autonomy. He can make decisions on his own and implement those decisions.
3. Multitasking CapabilityAgentic systems are often designed to be able to perform multiple tasks. They can prioritize between different tasks and use resources in the most efficient way.
4. Process-Oriented vs. Outcome-Oriented: Whereas traditional systems mostly focus on automating a particular process, agentic AI focuses on achieving a specific result and determines the path to that outcome itself.

Agentic Artificial Intelligence Working Principles

To understand how agentic artificial intelligence systems work, it is necessary to study their basic components and mechanisms of functioning.

Agent Architecture

A typical agentic artificial intelligence system consists of the following main components:

1. Sensors: They are components that collect data from the environment. This data can come from sensors, databases, web APIs, or other sources of information.
2. Knowledge Base: It is the database that contains the information that the system uses to carry out its task. This information may be pre-programmed or consist of information learned as the system runs.
3. The Reasoning Engine: It is the component that processes the collected data and makes decisions. Determines the most appropriate action using artificial intelligence techniques, machine learning algorithms and logic rules.
4. Actuators: They are the components that allow the system to interact with its environment. These can be engines in physical robots, as well as API calls, database operations, or user interface actions in software systems.

Decision Making Mechanisms

Agentic artificial intelligence systems can use a variety of decision-making mechanisms:

1. Rules-Based Decision Making: Simple decision-making mechanism based on predefined “if-if” rules.
2. Probabilistic Decision Making: Allows decision-making in uncertainty situations using models based on probability theory such as Bayesian networks.
3. Optimization-based Decision Making: Aims to make decisions that will maximize a specific utility function.
4. Learning-based Decision Making: Develops decision-making strategies by learning from experiences through techniques such as reinforcement learning.

Planning and Problem Solving

Agentic artificial intelligence systems can plan to achieve their goals and solve the problems they face:

1. Hierarchical Planning: Generates solutions by dividing complex tasks into smaller and manageable subtasks.
2. Reactive Planning: It can quickly adapt to changes in the environment, updating its plans.
3. Multi-Agent Planning: In cases where multiple agentic AI systems work together, they aim to achieve common goals by ensuring coordination.

According to Stanford University's Artificial Intelligence Index Report 2024, agentic AI systems with advanced planning algorithms can achieve 78% success rates in complex tasks.

Agentic Artificial Intelligence Application Areas

Agentic artificial intelligence technology finds a variety of applications in many industries:

Use in the Financial Sector

In the financial sector, agentic artificial intelligence systems are used in areas such as the creation of investment strategies, risk management, fraud detection and customer service.

For example, agentic buying and selling systems used in large investment banks can analyze market data in real time, detect trends and anomalies and make buying and selling decisions based on this information. These systems can increase profitability by exploiting micro-opportunities that human traders cannot capture.

Applications in Retail and E-Commerce

In the retail and e-commerce sector, agentic is used in areas such as artificial intelligence, inventory management, supply chain optimization, personalized marketing and customer experience improvement.

For example, agentic artificial intelligence systems used in major e-commerce platforms can analyze customer behavior and preferences, offering specific product recommendations to each customer. In addition, by making demand forecasts, it can optimize inventory levels and make supply chain operations more efficient.

Agentic Artificial Intelligence in Production Processes

In the manufacturing sector, agentic artificial intelligence is used in areas such as optimization of production lines, quality control, preventive maintenance and supply chain management.

For example, agentic artificial intelligence systems used in smart factories can monitor production processes in real time, detect potential problems in advance and optimize production parameters. This allows production efficiency to increase and costs to decrease.

Applications in Telecommunications

In the telecommunications sector, agentic is used in areas such as artificial intelligence, network management, customer service, fraud detection and infrastructure planning.

For example, agentic artificial intelligence systems used in telecommunications companies can monitor network traffic in real time, detecting potential problems in advance and optimizing network resources. Also, by analyzing customer behavior, it can provide personalized services and improve customer satisfaction.

The Future of Agentic Artificial Intelligence Technology

Agentic artificial intelligence technology continues to develop rapidly and is expected to become more widespread in the future. Here are some important trends related to the future of this technology:

1. Federated Learning: Agentic artificial intelligence systems will be able to learn from different sources, using federated learning techniques, while maintaining data privacy.
2. Explainability: Future agentic artificial intelligence systems will have mechanisms to make their decisions more transparent and explainable.
3. Human-AI Collaboration: Agentic artificial intelligence systems will be developed to be able to collaborate more effectively with humans, complementing human capabilities in complex tasks.
4. Multi-Agent Systems: Complex systems in which multiple agentic artificial intelligence systems work together performing different tasks will become widespread.

According to IDC's “Future of AI: Agents Everywhere” report, the agentic AI market is expected to reach $78 billion by 2030 with an annual growth rate of 34.8%.

Today's rapidly developing agentic artificial intelligence technology provides organizations with a competitive advantage and transforms business processes. By optimizing decision-making processes, increasing efficiency, and creating new business models, this technology offers organizations significant opportunities on their digital transformation journey.

As agentic artificial intelligence systems become even more intelligent, adaptive and able to collaborate with people in the future, their impact in the business world will also increase. Therefore, it is of great importance for organizations to understand this technology and incorporate it into their strategic plans. When applied correctly, agentic artificial intelligence technology emerges as a powerful tool that will enable organizations to be more efficient, innovative and competitive.

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Bibliography

1. Forrester Research. (2023). “The Rise of AI Agents”.

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