Assalamu‘alaikum wr. wb.
Hello everyone! Besides Vibe Coding and Model Context Protocol (MCP), there is another new Term related to AI Technology, and even related to AI Agents, namely Agentic AI. So, what exactly is Agentic AI? Here is an explanation.
Article Sources : en.Wikipedia.org, Botpress.com, Uipath.com, Aisera.com (Blog), Salesforce.com, Dicoding.com (Blog), and Heracx.ai
The rapid development of AI is now bridging the gap between science fiction and the real-world needs of businesses. Initially, organizations relied on predictive AI to identify patterns in data and forecast future events. Then, generative AI emerged with the ability to create various types of new content, ranging from written text to programming code. Today, the technology landscape is entering the era of Agentic AI, a stage in which AI not only generates information but is also capable of acting and responding autonomously.
The key advantage of autonomous AI agents lies in their ability to understand the context of their environment, make decisions independently, learn from experience, and adapt their behavior over time — rather than relying solely on statistical predictions.
Agentic AI is regarded as a strategic technology for the future because it emphasizes autonomy and flexibility. With strong integration into data platforms and business systems, this technology has the potential to revolutionize the healthcare, financial, and manufacturing sectors through intelligent workflow automation. AI is no longer merely an assistive tool but is taking on the role of a digital workforce capable of making decisions and adapting quickly and effectively.
A. Definition of Agentic AI
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| Illustration of Agentic AI Platform Architecture |
Agentic AI is a form of artificial intelligence designed to operate autonomously in achieving specific goals, without requiring continuous human guidance or intervention. Unlike conventional AI, which only responds to inputs or performs tasks according to predefined instructions, Agentic AI is capable of making its own decisions, determining priorities, and adapting its behavior according to continuously changing conditions.
The fundamental difference between Agentic AI and AI in general lies in the level of autonomy it possesses. Traditional AI operates within clearly defined instructions, such as providing answers, generating visuals, or presenting recommendations based on user requests. Meanwhile, Agentic AI has an internal drive for initiative, allowing it to identify problems, formulate problem-solving approaches, design action steps, and make adjustments without waiting for direct instructions.
The principles of autonomy and initiative form the foundation of Agentic AI. These systems do not merely process data but also understand the ultimate objectives and act proactively to achieve them, resembling intelligent agents capable of thinking and acting independently to obtain the best possible outcomes.
B. Differences Between Agentic AI and AI Agents
Agentic AI and AI agents are closely related. Agentic AI refers to the capability or characteristic of autonomy, while AI agents are software implementations that apply these capabilities in practice.
Therefore, Agentic AI can be understood as an umbrella concept that emphasizes autonomy and the ability to take action, whereas AI agents are practical implementations of that concept. However, Agentic AI does not always take the form of AI agents alone; it can also be implemented as integrated systems, frameworks, or large-scale platforms.
Although they represent 2 (Two) different AI Capabilities, Agentic AI and generative AI—which is used to generate text, images, music, code, and other content—are often used together.
Agentic AI focuses on autonomous decision-making, and some of the decisions made during the process may involve generative capabilities. For example, an Agentic AI system can leverage generative AI to :
- Create marketing messages tailored to users' needs
- Provide dynamic product recommendations through a conversational AI interface
C. How Agentic AI Works
Agentic AI operates through a core cycle consisting of several key components that enable autonomous agents to pursue and complete goals from start to finish. This process is driven by a large language model (LLM) as the central control system, acting as the “brain” of the agent and enabling it to reason, plan, and make decisions.
The operational foundation of Agentic AI relies on several fundamental concepts :
1. Planning
Breaking down a complex high-level goal (for example, “Resolve a customer billing dispute”) into a series of smaller, manageable, and executable steps (for example, “Search the knowledge base,” “Verify payment history in the CRM,” and “Draft a resolution email”).
2. Reasoning
The ability to evaluate the current situation, understand the task, select the appropriate tools, and determine the best next action. At this stage, the intelligence of the LLM plays a key role.
3. Tool Use
The agent’s ability to connect to external systems through APIs or other interfaces to perform actions. These “tools” can include CRM systems, programming environments, and data query engines.
4. Memory
The system must retain the context of previous actions and observations to maintain consistency throughout multi-step workflows. This includes short-term memory (the context of the current step) and long-term memory (learned knowledge and past outcomes).
5. Reflection
The process of observing the results of an action, comparing them with the intended goal, and adjusting the plan if the results are not satisfactory. This mechanism enables self-correction and continuous improvement.
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Through these concepts, AI agents can solve complex problems through the following continuous five-step cycle :
1. Perceive
The AI agent collects and interprets information from its environment, such as user instructions, sensor data, or database records, to identify the goal and current conditions.
2. Reason
The LLM guides the reasoning process by understanding the task, developing an initial plan, and coordinating the specialized models or tools required.
3. Act
The agent performs tasks by connecting to external systems, such as CRM, financial systems, or manufacturing control systems, through APIs. Built-in safeguards help ensure security and compliance.
4. Observe & Reflect
The agent observes the results of its actions and evaluates whether those actions bring it closer to its goal. If not, the agent learns from the failure and adjusts its strategy.
5. Iterate & Collaborate
This iterative cycle enables continuous refinement. In multi-agent systems, multiple agents with different areas of specialization can work together, share information, and coordinate actions to solve larger and more complex problems.
D. Key Characteristics of Agentic AI
AI agents are fundamental elements of the Agentic AI architecture and serve as the driving force behind the future of intelligent automation. At its core, Agentic AI focuses on the seamless integration of various specialized agents, each designed for a specific purpose.
Agent Assist enables AI agents to handle daily tasks efficiently through direct collaboration between humans and AI. These agents collect data from previous tickets and help helpdesk personnel resolve issues more quickly.
Some AI agents excel at aggregating and presenting information from multiple sources, making them particularly suitable for dynamic and minimally regulated environments. Other agents are carefully designed to operate within strict compliance frameworks, ensuring that every action they perform remains consistent with applicable standards.
In addition, workflow-focused agents serve as the brains behind automation. These agents intelligently generate and execute workflows across applications, independently identifying the appropriate APIs, determining the most optimal sequence, and fulfilling user requests with precision.
The primary strength of Agentic systems lies in the orchestration of these diverse agents. Their architecture allows agents to be grouped into logical domains, making deployment and management easier for different teams within an organization. As a result, each team can work independently while remaining aligned with a unified AI strategy that strengthens the business as a whole.
Another important feature is the ability to integrate external agents that are not built directly on the platform. This flexibility allows companies to continue innovating by adopting new technologies without disrupting existing systems. The goal is to build an ecosystem in which all AI agents work together as a harmonious system and deliver optimal performance across all areas of the business.
These AI agents can be classified into 4 (Four) Types :
- Generative Information Retrieval Agent: A knowledge-providing agent for low-regulation environments or topics.
- Prescriptive Knowledge Agent: A knowledge-providing agent for highly regulated environments or topics.
- Dynamic Workflow Agent: An action-oriented agent that executes and coordinates processes.
- User Assistant Agent: An agent that directly assists users in completing their daily tasks.
E. Komponen Agentic AI
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| Agentic AI Components |
There are various types of AI-powered agents that serve as the fundamental building blocks of agentic systems. Let us take a closer look at the components of an agent. An AI agent in an agentic system consists of 3 (Three) Main Components :
- A Prompt
- Memory for the Agent
- Tools
1. Prompt
A prompt serves as the primary guideline that defines the agent’s objectives, rules, and operating procedures. It functions like a roadmap for a multi-agent system, ensuring that each agent understands its role and works toward the same goals. By distributing tasks among multiple agents, system complexity can be managed because each agent only needs to follow clear and focused instructions.
2. Memory
Memory is the foundation of an LLM agent’s intelligence. Through memory, an agent can retain context, learn from the outcomes of previous interactions, and use those experiences to make better decisions. This form of memory can be as simple as storing conversation history or as sophisticated as storing semantic summaries of previous interactions.
3. Tools
Tools provide agents with practical capabilities to take action. With the help of APIs, functions, or external services, agents can execute various tasks directly and efficiently. Understanding these three components helps us see how a single agent can function as a complete system.
F. Types of Agentic AI
There are various approaches to implementing Agentic AI. Below are five of the most common types of Agentic AI, along with real-world examples of AI agents and the systems they operate.
1. Reactive Agentic AI
Definition: An AI system that operates by responding to specific stimuli or conditions without having long-term memory or learning capabilities.
Examples: Chatbots with predefined question flows, rule-based recommendation systems.
Reactive Agentic AI is highly reliable for handling simple tasks quickly and precisely. This type is ideal for situations that require instant responses based on predefined scenarios, such as answering frequently asked questions (FAQs) or recommending products.
2. Deliberative Agentic AI
Definition: An AI system that relies on reasoning and planning processes when making decisions, often while considering long-term consequences.
Examples: Autonomous vehicles that manage traffic routes, AI-powered supply chain management systems.
Deliberative AI leverages logic and prediction to handle complex problems, ensuring that decisions remain aligned with broader objectives. These systems are crucial for applications that require strategic planning and the ability to adapt to changing conditions.
3. Interactive Agentic AI
Definition: AI designed to communicate and interact with humans and other systems, typically in continuously changing environments.
Examples: Virtual assistants, collaborative robots (cobots) in industrial sectors.
The primary focus of interactive Agentic AI is to create seamless interactions between humans and machines. These systems play an important role in situations where understanding user needs and providing appropriate responses are key factors for success.
4. Adaptive Agentic AI
Definition: An AI system capable of learning and evolving over time through data and feedback, and subsequently adapting its behavior.
Examples: AI agents for personalized learning, dynamic pricing systems on e-commerce platforms.
Adaptive AI uses data as the foundation for continuously improving the quality of its decisions and actions. This type is highly effective in situations that demand a high degree of flexibility and continuous improvement to achieve the best possible outcomes.
5. Multi-Agent Systems (MAS)
Definition: A collection of AI agents that cooperate or compete with one another to achieve shared or individual goals.
Examples: Swarm-based robotics, distributed AI systems for smart power grids.
Multi-agent systems involve multiple AI entities interacting with one another to solve complex, large-scale problems. This approach is highly effective in distributed environments, where tasks can be completed more efficiently through collaboration and diverse strategies.
G. Examples of Agentic AI
The use of Agentic AI is becoming increasingly widespread and is having a significant impact across various industry sectors. Below are several concrete examples of Agentic AI applications in everyday activities :
1. Intelligent Virtual Assistants
In today’s work environment, virtual assistants powered by Agentic AI are no longer limited to answering basic questions. These systems can manage schedules, prioritize tasks, and even negotiate automatically with other parties to adjust meeting schedules or deadlines. Thanks to their autonomous decision-making capabilities, these assistants can operate according to users’ needs without requiring repeated instructions, thereby improving work efficiency both personally and professionally.
2. Industrial Automation
In the manufacturing sector, robots that adopt Agentic AI enable production processes to operate more adaptively. When problems or failures occur on a production line, the system can automatically adjust the workflow to reduce downtime without human intervention. With its ability to adapt in real time, Agentic AI plays a major role in improving productivity and efficiency across industries, from light to heavy manufacturing.
3. Customer Service: Indonesian AI Chatbot
In the field of customer service, HERA is one example of an Agentic AI application. More than just a conventional chatbot, HERA can understand conversational context, determine the best course of action to resolve customer complaints, and offer alternative solutions without immediately transferring the conversation to a human agent. Through its autonomous and responsive approach, HERA helps companies provide faster, more personalized, and more effective customer service while also increasing customer satisfaction and loyalty.
With these capabilities, Agentic AI not only simplifies various tasks but also creates opportunities for innovation across industries, making operational processes smarter, more responsive, and more adaptive to change.
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Wassalamu'alaikum wr. wb.