{"id":89384,"date":"2025-06-10T11:54:52","date_gmt":"2025-06-10T08:54:52","guid":{"rendered":"https:\/\/intellias.com\/?post_type=blog&p=89384"},"modified":"2025-06-10T11:54:52","modified_gmt":"2025-06-10T08:54:52","slug":"agentic-ai","status":"publish","type":"blog","link":"https:\/\/intellias.com\/agentic-ai\/","title":{"rendered":"What Is Agentic AI? How to Build an Agentic AI Strategy for Your Business"},"content":{"rendered":"
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Agentic refers to someone or something capable of achieving outcomes independently (\u201cfunctioning like an agent\u201d) or possessing such ability, means, or power (\u201chaving agency\u201d). <\/p>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\n\t\t\t\t\tMerriam-Webster<\/a><\/span> <\/span>\n\t\t\t\t<\/div>\n\t\t\t<\/blockquote>\n\t\t<\/section>\n

While we are still wrapping our heads around generative AI, technology has taken another step forward with agentic AI. Now, artificial intelligence that can achieve results independently surely has some Terminator vibes. But don\u2019t worry. While capable of autonomous decision-making, agentic AI is currently used to only carry out routine tasks, accelerating everyday processes.<\/p>\n

Still, the idea of artificial intelligence working without human supervision, solving problems independently, and learning in the process seems rather fascinating \u2014 so fascinating that up to 50% of businesses that use generative AI plan to launch agentic AI pilots by 2027. This research by Deloitte<\/a> also highlights increasing investor interest in agentic AI technology: funding of enterprise agentic AI startups reached US$2 billion between 2022 and 2024.<\/p>\n

The public has also shown increased interest in the concept of agentic AI. Google Trends<\/a> records a sharp spike in search queries for \u201cagentic ai\u201d at the end of 2024, indicating that people want to know more about intelligent apps that can work on their own.<\/p>\n

Growing public interest to agentic AI as shown by Google Trends<\/strong><\/p>\n

\"Sharp<\/p>\n

Source<\/a><\/em><\/p>\n

In view of this rapid expansion of artificial intelligence across industries and applications, should we be concerned? Apprehensive? Excited? Enthusiastic? All of the above? Let\u2019s dig deeper into what agentic AI is, how it operates, and what it might mean for humankind.<\/p>\n

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What is agentic AI?<\/h2>\n

The term agentic AI describes artificial intelligence systems that can operate with a high degree of autonomy. With minimum human oversight, an AI agent can make decisions on its own based on real-time conditions. Moreover, agentic AI learns in the process, dynamically adjusting its actions to the situation and refining its approach.<\/p>\n

Leveraging machine learning, reinforcement learning, and symbolic reasoning, agentic AI makes informed decisions and can achieve relatively complex goals. Advanced decision-making frameworks allow agentic AI tools to define priorities, manage uncertainty, and handle constraints. Interacting with the environment in real time, agentic AI tools detect changes, analyze patterns, and adapt strategies based on evolving scenarios. As a result, their decision-making abilities improve over time.<\/p>\n

How does agentic AI work?<\/h2>\n

To make decisions in a dynamically changing environment, agentic AI follows a four-step process, allowing it to act based on the current circumstances and adjust in response to future changes.<\/p>\n

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  1. Perceive<\/strong>. Agentic AI tools use sensors, APIs, and data feeds to gather information about their environment. In the process, they can ingest various types of data: images, text, video, audio, and real-time data streams in different formats. Agentic AI continuously processes incoming data, detects patterns, recognizes features\/objects, and identifies opportunities for further actions.<\/li>\n
  2. Reason<\/strong>. At the core of an agentic AI tool is typically a large language model (LLM) that processes collected data using advanced reasoning techniques such as probabilistic models and deep learning. At this stage, AI evaluates potential results, sets priorities, and chooses the optimal course of action to reach the defined goal.<\/li>\n
  3. Act<\/strong>. Based on the defined action plan, agentic AI executes the outlined tasks using integrations with external software and by interacting with human users. The range of possible actions is quite extensive, from sending notifications to adjusting workflows or controlling automated systems. All AI actions contribute to achieving predefined goals while adapting to the real-time environment.<\/li>\n
  4. Learn<\/strong>. Agentic AI constantly evaluates the outcomes of its actions through reinforcement learning and feedback loops, using this analysis to refine future decision-making processes by updating the LLM. The learning stage helps the system to improve efficiency and accuracy and achieve sustained optimization.<\/li>\n<\/ol>\n

    \"Agentic<\/p>\n

    Depending on the complexity of tasks defined for the agentic AI system, it may consist of one or several AI agents.<\/p>\n

    Single-agent systems<\/h3>\n

    In a single-agent system, one independent AI component operates in a defined environment. The agent works according to a step-by-step action plan created using the LLM capabilities and designs a strategy focused on completing each step and progressing toward the goal.<\/p>\n

    Single-agent systems are simple to set up, as they require no communication with other agents. They are also easy to control and make it easy to maintain the consistency of AI decision-making<\/a>. However, this simplicity has a flip side: a restricted ability to perform complex tasks that call for high adaptability. In addition, single-agent systems are harder to scale due to their limited memory and processing capacity.<\/p>\n

    Multi-agent systems<\/h3>\n

    A multi-agent system combines several AI agents, each responsible for a specific task. These AI agents collaborate to achieve a common goal.<\/p>\n

    A multi-agent architecture provides much broader opportunities than a single-agent architecture. Multi-agent systems can perform complex tasks that require solving different problems. Communication between agents allows for coordinating their actions and sharing acquired knowledge. Some agents may be responsible for collecting data, others for decision-making, and others for execution.<\/p>\n

    Besides the ability to handle complex problems, multi-agent systems can easily be scaled and are highly adaptable and resilient. Additional agents can be integrated as necessary, increasing the system\u2019s capacity. Such solutions can quickly adjust their actions to the evolving environment, improving overall flexibility and efficiency.<\/p>\n

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    Not sure how to get started with artificial intelligence? Follow the guidelines of our AI adoption framework.<\/p>\n

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    Types of AI agents in an agentic architecture<\/h2>\n

    AI agents are the actual performers of various tasks within an AI-automated process. Each agent is capable of accomplishing certain tasks independently. An agentic AI architecture unifies different agents \u2014 systems, devices, or software \u2014 into a comprehensive organization designed to achieve predefined goals in an automated process.<\/p>\n

    Based on their capabilities, there are several types of AI agents:<\/p>\n