Agentic refers to someone or something capable of achieving outcomes independently (“functioning like an agent”) or possessing such ability, means, or power (“having agency”).
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’t worry. While capable of autonomous decision-making, agentic AI is currently used to only carry out routine tasks, accelerating everyday processes.
Still, the idea of artificial intelligence working without human supervision, solving problems independently, and learning in the process seems rather fascinating — so fascinating that up to 50% of businesses that use generative AI plan to launch agentic AI pilots by 2027. This research by Deloitte also highlights increasing investor interest in agentic AI technology: funding of enterprise agentic AI startups reached US$2 billion between 2022 and 2024.
The public has also shown increased interest in the concept of agentic AI. Google Trends records a sharp spike in search queries for “agentic ai” at the end of 2024, indicating that people want to know more about intelligent apps that can work on their own.
Growing public interest to agentic AI as shown by Google Trends
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’s dig deeper into what agentic AI is, how it operates, and what it might mean for humankind.
What is agentic AI?
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.
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.
How does agentic AI work?
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.
- Perceive. 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.
- Reason. 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.
- Act. 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.
- Learn. 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.
Depending on the complexity of tasks defined for the agentic AI system, it may consist of one or several AI agents.
Single-agent systems
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.
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. 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.
Multi-agent systems
A multi-agent system combines several AI agents, each responsible for a specific task. These AI agents collaborate to achieve a common goal.
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.
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’s capacity. Such solutions can quickly adjust their actions to the evolving environment, improving overall flexibility and efficiency.
Types of AI agents in an agentic architecture
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 — systems, devices, or software — into a comprehensive organization designed to achieve predefined goals in an automated process.
Based on their capabilities, there are several types of AI agents:
- Simple reflex agents perform predefined actions in response to specific inputs. They only act under preprogrammed conditions and do not interact with other agents. Simple reflex agents are not adaptable; therefore, they cannot act properly in a situation they have not been programmed for. The most common example of a simple reflex agent is an automated door that opens when a sensor detects a person in front of it.
- Model-based reflex agents store memory of their past states, maintaining a model of their environment and updating it with any new information they receive. Their actions are based on the current model and its previous states, which enables better decision-making and adaptability to dynamic changes. Typical examples of model-based reflex agents range from robot vacuum cleaners to self-driving cars, which store a model of a house or driving environment and use it to adjust their actions.
- Goal-based agents make decisions based on predefined goals. They evaluate actions available to them to select those capable of achieving a particular goal. Goal-based agents are highly adaptable to changing environments and can dynamically adjust their course of action. Such AI agents have multiple real-life applications: AI-powered robots can plan their movements according to set goals; video game non-player characters (NPCs) can adapt to player actions; energy distribution balancing agents in smart grid systems can optimize energy supply based on predicted demand.
- Utility-based agents act based on a utility function, assigning value to actions and choosing those that bring the maximum benefits. They evaluate several possible outcomes and measure the degree of success for each, then select the actions that lead to the most successful outcome. Modern AI-powered applications have multiple uses for utility-based agents: diagnostics and personalized treatment recommendations in healthcare; route planning for autonomous vehicles; strategy planning in video games.
- Learning agents improve their performance by learning from their past actions and adapting strategies based on feedback and data updates. Rather than follow predetermined instructions, learning AI agents dynamically adjust their decision-making relying on their experience. Such AI agents are capable of performing rather complex tasks such as driving personalized recommendations, evaluating market data to identify trends, or monitoring patient health conditions.
What is the difference between agentic AI and traditional AI?
The main difference between agentic AI and traditional AI is the ability of agentic AI to work autonomously and make independent decisions:
- Autonomy. Traditional AI tools need human intervention to operate, while agentic AI can be independent in its decision-making, adjusting its actions based on real-time data.
- Adaptability. Traditional AI relies on preprogrammed rules and structured training data. In turn, agentic AI dynamically adapts to the current situation, learning from experience and refining its decision-making accordingly.
- Decision-making complexity. Traditional AI’s rule-based approach limits its ability to operate beyond predefined instructions. Agentic AI is empowered with reinforcement learning, symbolic reasoning, and probabilistic models that enable it to navigate uncertainty and optimize its actions autonomously.
- Interaction with the environment. Traditional AI reacts to inputs based on static models. Agentic AI takes a proactive approach, engaging with the environment, perceiving changes, adapting its actions accordingly, and constantly learning in the process.
- Task scope. Traditional AI handles isolated well-defined tasks such as image recognition or content creation. Agentic AI has much broader objectives that include strategic decision-making and achieving specific goals.
Benefits of agentic AI
What are the risks of implementing agentic AI?
While agentic AI solutions have obvious benefits, businesses that consider using them to enhance efficiency and improve decision-making must be aware of the associated risks:
- Lack of explainability and transparency. Agentic AI’s decisions become increasingly hard to understand as the goals get more complex. Even AI creators may find it hard to see why a certain decision was made. This may present high and even unacceptable risks in such industries as healthcare, defense, and finance.
- Bias and ethical concerns. Data used in the training of agentic AI may be biased or incomplete, causing the resulting decisions to contain the same bias or lack of integrity. Non-compliance with adopted ethical standards may cause serious consequences when agentic AI is used in criminal justice, loan approval, or medical treatment recommendations.
- Regulatory challenges. Globally, AI regulations are still at the early stages of development and approval. For businesses adopting agentic AI, this may present a risk of future non-compliance as regulations evolve.
- Security risks. Autonomous AI solutions can be vulnerable to cyber-attacks. Hackers may obtain access to the LLM at the core of an agentic AI system and manipulate it to alter its decisions. Such threats are especially serious for AI-enabled military, healthcare, and financial applications.
- Over-reliance on AI. The practice of trusting AI-generated decisions without careful verification may lead to increased risks in critical scenarios. In such cases, it may become difficult (or even impossible) for people to override AI decisions, which may lead to severe consequences.
- Job displacement. With AI taking over multiple manual tasks, some positions may become obsolete with people getting dismissed and their functions replaced by AI. Workers made redundant by AI will have to be retrained in other skills and will need assistance with adapting to the changed circumstances.
These risks pose a serious challenge to businesses. However, they can be mitigated with a responsible AI approach in which AI-powered applications are supported with a comprehensive governance framework that ensures ethical decision-making, regulatory compliance, and trust.
Industries that can benefit from agentic AI
In the last decade, robotic process automation (RPA) and AI have firmly established themselves in multiple industries. Now, a new wave of transformation is coming with the growing adoption of agentic AI, which is poised to find many uses across industries.
Best practices of using agentic AI
To maximize the benefits of agentic AI, we recommend that businesses follow certain best practices:
- Define clear objectives. Establish goals that you would like to achieve with the help of agentic AI and define measurable KPIs to evaluate your success.
- Implement data governance. Introduce a data governance framework to ensure data integrity and quality, security, and regulatory compliance.
- Ensure human oversight. Provide opportunities for human intervention when agentic AI is involved in critical decision-making.
- Optimize for continuous learning. Implement learning mechanisms that enable AI solutions to improve decision-making by learning from interactions with the real world.
- Focus on security. Protect AI systems from cyber attacks and continuously monitor and update your security mechanisms.
- Encourage cross-functional collaboration. Engage experts from various domains in your organization to ensure that AI-powered processes align with business needs and goals.
How to implement agentic AI in your organization
If you’re considering introducing agentic AI in your organization, start with developing a working strategy and an actionable roadmap, taking into account all the details and specifics of your business. Here’s a step-by-step plan for enhancing your business with agentic AI.
| Assess your business needs | Identify areas where agentic AI can bring the highest value and define problems it can help you resolve. Start with the lowest-risk use cases to test your strategy. |
|---|---|
| Develop a strategic AI adoption roadmap | Define goals and timelines and plan the budget for your agentic AI project. |
| Evaluate your data readiness | Review your data governance framework to ensure data quality and consistency. |
| Assess your business readiness | Verify your technology components and integrations to ensure their ability to work in an AI-driven process. Evaluate your team’s skills and plan necessary training. |
| Choose a reputable AI vendor | Partner with a professional provider of AI services and work on customizing AI solutions to meet your needs. |
| Monitor and optimize AI performance | Continuously evaluate your AI systems to discover optimization opportunities. |
Major providers of agentic AI tools on the global market
What does the future hold for agentic AI?
So far, agentic AI has shown promise to improve the efficiency and quality of business processes. As technology evolves, we may see deeper integration of agentic AI into real-life applications in multiple industries. Its role in business flows is likely to transform and expand:
- Greater autonomy. Increased adoption of agentic AI will lead to a greater degree of AI-powered decision-making, reducing the need for human intervention.
- Integration with IoT and edge computing. Enhancing IoT networks with AI agents will allow for real-time decision-making at the data source, reducing latency.
- Multi-agent collaboration. Agentic AI consisting of several agents will become more integrated, increasing the efficiency of information exchange and task coordination.
- Workforce transformation. As agentic AI takes over manual tasks, workers will need to learn AI-related skills or retrain to take advantage of other opportunities.
At the same time, businesses need to keep in mind that the advance of AI technology calls for additional regulatory guidelines and frameworks.
Are you ready for agentic AI?
It seems that our present and future are closely tied with artificial intelligence, which is likely to define the success of many businesses. If you want to embrace all the benefits of AI but aren’t sure how to get started, we’re here for you. Intellias AI experts will guide you along the entire AI adoption path, helping you implement agentic AI effectively and responsibly.
