The evolution of AI in recent years has pushed corporations to adopt innovative technologies into their business strategies. After all, staying on the sidelines can mean losing your competitive edge. Nonetheless, the new wave of artificial intelligence promises an unprecedented level of agency and automation.
Now, AI can not only predict outcomes and solve complex problems — it can make decisions and execute them without human intervention. Gartner reports that 33% of enterprise software applications will use agentic AI by 2028. So, what does this development entail?
In our article, we will explain what agentic artificial intelligence is and how it works, the role of smart agents in business growth, its benefits, and real-world applications. We will also explore the future opportunities this technology spells for organizations to keep you in the loop. Let’s start with the basics.
What Is Agentic AI?
Leveraging AI to perform certain tasks is not a new concept. However, agentic artificial intelligence takes it a step further. Unlike traditional systems that operate on pre-defined rules, agentic systems can “think for themselves” and execute actions through contextual awareness. This makes them especially valuable in dynamic environments where limited standardization is not enough.
At its core principles lies autonomy, which means agentic systems do not require extensive human supervision to complete tasks. While other types of AI are capable of performing with minimal human intervention, they are trained to act in specific scenarios, which typically does not allow them to adapt to unforeseen circumstances.
This leads to the next primary principle of such systems: goal-oriented decision-making. Essentially, the program evaluates contextual cues to make optimal decisions that lead to the ultimate goal set by the user. This means that if the system encounters problems, it can solve them on its own based on the data available to it.
With the help of agentic AI companies, you can apply for AI development services to implement cutting-edge systems into your business. For now, let’s dive deeper into exploring what makes this technology unique.
Core Characteristics
Generally, agentic artificial intelligence can be described by its five main characteristics. Here is what they are.
- Perception
In order to interpret inputs, the model has to be able to perceive them. In this case, the systems use sensors to capture information. For example, it can use cameras powered by Computer Vision to perceive visual data. Microphones can be employed to capture auditory inputs by utilizing Natural Language Processing.
Additional sensors can also be used. For instance, a self-driving car can analyze data from a dashboard or GPS to facilitate decision-making. Oftentimes, a combination of the most common sensors is deployed to create a comprehensive perception of the environment.
- Reasoning
Based on the captured data, the system employs logical reasoning to evaluate the information and come to the best possible conclusion that drives it closer to the established goal. Basic programs often operate on an if-then logic, meaning if a certain condition has been met, then the following action needs to be taken.
Advanced AI agentic software operates on probabilistic models, such as the Markov decision process (MDP) and Bayesian Networks. These are mathematical models used to determine the probability of certain events based on the relationship of random variables. As a result, conclusions can be drawn even if the data is scarce.
- Action
The following step in the process is to execute the action according to the conclusions made in the previous stages. Depending on where you utilize the system, it can perform a variety of tasks. For example, physical actions, like moving equipment or steering the wheel, can be taken when driven by robotics.
Digital actions can also be conducted, like sending reports, responding to the user’s queries, activating devices, and so on. At its core, the model interacts with the software via application programming interfaces (APIs) to manage the execution autonomously.
- Learning
Agentic AI systems are capable of learning over time based on previous experiences. Generally, reinforcement learning is the most widespread technique as it allows the system to gain knowledge on a trial-and-error basis. In some cases, human supervision can be involved in providing feedback to the AI, helping it understand which actions were rewarded and which were penalized.
- Adaptability
A combination of all the previous characteristics results in the major advantage of agentic artificial intelligence, which is the capacity to adapt to changing circumstances. This process can be made dynamically, meaning AI reacts to the environment and adjusts according to changes. It can also be done manually by introducing updates and training on new data as the business grows.
Key Mechanisms
So, what is agentic AI? By now, you should have a fundamental understanding of how it works and the steps it takes to operate effectively. Nevertheless, we want to briefly explain the underlying mechanisms that make these processes possible.
- Machine Learning (ML)
The foundation of agentic technology is Machine Learning. It’s used to train the model on vast datasets to aid in making decisions. There are a few types of ML: supervised learning, unsupervised learning, and reinforcement learning, which we already mentioned.
The first one utilizes labeled data, where the conclusions are pre-defined. Conversely, the second type employs unlabeled data, allowing the system to define outcomes based on outliers and hidden patterns. The last one operates on feedback and learns to adapt to maximize rewards.
- Natural Language Processing (NLP)
This technology aims to understand and interpret natural human language. Typically, it can process text and speech while maintaining contextual nuances and recognizing sentiment. Through Natural Language Processing, users can produce inputs via text or voice and receive outputs tailored to their request.
- Large language models (LLMs)
A subset of NLP, Large Language Models are mostly known because of generative AI tools like ChatGPT. However, in agentic systems, LLMs can power processes that are responsible for holding human-like conversations. For instance, in customer support, they can understand nuanced user inquiries and aid in performing tasks accurately.
The Role of Smart Agents in Business Growth
Leading agentic AI companies are driving innovation in the workflow by integrating smart agents. While they might sound similar to agentic artificial intelligence, these are actually separate concepts.
At their core, smart agents or AI agents are programs driven by LLMs that aim to autonomously carry out tasks. However, traditional agents lack the capability to adjust to situations dynamically and create subtasks to achieve goals. They are programmed explicitly to follow the established rules, requiring human supervision.
Typically, they are used to carry out simple procedures based on pre-determined instructions, such as answering customer support questions or scheduling events on the calendar. While basic smart agents have the capacity to learn and adapt, it is generally done through manual updating and still necessitates specified rules.
The agentic AI-powered smart agents, on the other hand, can autonomously make decisions that drive them closer to reaching objectives without following explicit directions. Such programs learn from data and reinforcement learning, making them a powerful tool in an agentic system.
Smart agents enhanced with this technology can work in harmony with other systems and agents alike, creating a collaborative environment that prioritizes effective goal achievement. Speaking of which, let’s get a closer look at single-agent and multi-agent systems, discovering how each influences business growth.
Single-Agent Systems
As the name suggests, a single smart agent is implemented to perform a designated task of lower or medium complexity, which does not require multi-layered procedures or collaboration between systems. A user inputs the prompt, setting a goal for the agent, and the program enacts the steps we have talked about in the previous section.
These are ideal in environments where straightforward assignments take a lot of time to complete manually and would benefit from automation. The single-purpose principle of these smart agents is also easier to integrate into the system and manage over time. As a result, businesses can delegate specific tasks to agents where precision is key, freeing up the time to focus on other essential operations.
To put it into perspective, such AI agentic programs are perfectly suited for virtual assistants to help carry out mundane tasks like email management, sending reminders, etc. On the business side, e-commerce stores can integrate a single smart agent to track inventory and predict demands. Using this data, the program can then automatically place orders when stocks are running low.
Multi-Agent Systems
In contrast to the previous type, multi-agent systems (MAS) deploy several agents that work simultaneously to achieve a shared goal. Each agent in such a system is responsible for handling their own designated responsibilities and communicating with others to coordinate their efforts.
This type of ecosystem is more suitable for businesses that tackle complex and dynamic workflows, which call for the autonomous execution of several interconnected operations. For example, MAS can be employed to supplement AI investment. One agent can be responsible for tracking stock markets and volatility, another can address risk assessment, and so on.
When each program in such agentic AI systems has completed its assignment and the optimal conditions are reached, automatic investments can be executed without human intervention. This same structure applies to other businesses, like supply chain management, where agents control different types of procedures to achieve the objective.
Unlike single agents, MAS is more intricate to implement, but it offers a unique set of advantages. For instance, as the business grows, more agents can be added to the system to support scaled operations. Additionally, MAS is built for resilience, which means if one agent fails, another can step into their role and carry out their tasks without losing efficiency.
As you can see, both options offer growth opportunities. The decision of which you should consider will depend on the scale of operations that need to be automated. You can reach out to AI consulting services to determine the best course of action for your company.
Key Benefits of Agentic AI for Businesses
The rapid development of artificial intelligence promises to provide solutions to problems that different types of businesses often face. With agentic technology in particular organizations can gain a competitive advantage by automating significant portions of operations, improving decision-making, and goal achievement. On top of that, such systems can deliver better personalization of experiences, scale more effectively, and supplement the workforce.
In this case, AI can enhance both internal and customer-facing processes. So, let’s examine more closely the five main benefits this tech brings to the table.
Workflow Automation
Agentic AI companies enable corporations to enter a new level of efficiency by automating significant and time-consuming parts of the workflow. With an understanding of the underlying principles of agentic systems, it’s easy to see how they can alleviate the workload and accelerate mundane procedures like report generation and submission on a daily basis.
On top of that, complex operations, such as coordinating supply chains, can propel enterprises to a higher grade of productivity. Models based on algorithms within machine learning can further decrease the rates of human error and the probability of negative outcomes if mistakes are made.
Proactive Decision-Making
Agentic systems dynamically adjust to changing variables without the need for supervision, unlike their rule-based counterparts. This allows them to make proactive decisions with no hesitation according to the probability of the best result. The predictive nature of such AI enables contextually-aware conclusions that streamline decision-making and faster response times.
Insights derived from agentic programs can also supplement human specialists, as artificial intelligence processes massive amounts of data within a few seconds or minutes. These findings could be used to adjust business strategies to reflect the best conditions or hidden opportunities.
Customer Experience Personalization
In customer support, AI agentic programs can deliver hyper-personalized experiences to consumers by deeply understanding and resonating with their inquiries based on their behavior and responses. In addition, smart agents are capable of resolving problems more efficiently without involving human professionals. This builds retention, as customers feel like their issues are addressed on a meaningful level.
For personalized services, such as tailored financial advice or recommendation engines, systems can evaluate a large set of user’s information and preferences, as well as anticipate their demand based on historical data. Thus, delivering highly customized experiences.
Scalability and Adaptability
Companies that expect rapid growth tend to struggle with software that fails to scale quickly without manual upgrading or complete system overhauls. The advantage of agentic programs lies in their autonomous adaptability, which enables them to leverage available data and scale appropriately to reflect the increasing workloads.
We also talked about multi-agent systems that likewise play a crucial role in scalability. Introducing additional agents to the architecture to tackle new processes is a more efficient approach than redesigning the entire system. It is further beneficial in overcoming operational inefficiencies during the scale-up without compromising performance or causing bottlenecks.
Workforce Support
The agentic AI-powered workplaces can reap the benefits of enhanced productivity, as this technology handles mundane tasks, allowing workers to focus on areas where human insight is irreplaceable. Of course, one of the major fears around artificial intelligence is job displacement. However, AI aims to streamline repetitive procedures and perform collaboratively with specialists.
While agency and independence are among its top advantages, human oversight can still be paramount to ensure that the system works toward its established goal. On top of that, data extracted by AI programs can substantially supplement business strategies and unveil opportunities that would otherwise go unnoticed.
Real-World Applications of Agentic AI Systems
To put the vast scale of practical applications of agentic technology into perspective, we need to overview them in the context of distinct industries. While the principle of how such systems work is understandable, it can be hard to imagine them in real-world scenarios. Looking through the lens of actual use cases, we can better grasp how AI is transforming business operations.
From analyzing extensive medical data for tailored patient care and automating financial reporting to personalizing educational programs and managing inventory, AI agentic paves the way for unparalleled efficiency. Let’s review the applications one step at a time.
Healthcare
The healthcare industry notoriously struggles with effectively documenting and managing patient data. AI-driven systems address this issue by autonomously collecting and analyzing information, such as patient medical history, diagnosis, imaging, prescriptions, and much more. As a result, valuable insights about a patient’s health can be extracted to deliver holistic treatment plans tailored to each person.
MAS plays a major role in this development since it is capable of coordinating efforts from several healthcare departments. Detailed reporting can also make capturing nuanced data less burdensome for the staff. Additionally, pharmaceutical research is enriched by agentic programs that can simulate drug reactions and their effects on the body.
Finance
We briefly touched on how agentic AI companies can revolutionize the financial sector, but there is more than meets the eye. By employing multi-agent systems, corporations can streamline critical processes in various areas. For example, such systems can enhance fraud detection by identifying anomalies in transactions and login attempts and flagging them as suspicious activity. Based on the feedback, the program can learn to better respond to future instances.
Single agents can also be useful in processes like expense reporting and compliance checks, eliminating the need for manual supervision. Personalized financial management is also supplemented, evaluating data points like risk tolerance and financial status to deliver customized advice to each client.
Retail
AI in eCommerce has various applications. Agents can be deployed to track the market conditions and competitor’s pricing to dynamically adjust the cost of goods in the store for maximum profitability. We also mentioned how agentic artificial intelligence can manage inventory and stock levels, automatically placing orders for resupply when certain items are running low.
Online stores can also implement advanced recommendation engines that tailor suggestions to consumers based on their behavioral patterns and offer customized discounts. When it comes to brick-and-mortar stores, agentic AI can power robots that serve as shopping assistants and help customers find the right products, answer their questions, and more.
Education
The educational sector can vastly improve administrative procedures and tailor learning programs for students with agentic technology. Single-agent systems can be utilized to streamline routine work like scheduling lessons and timetables, checking attendance, grading students based on their accomplishments, etc.
MAS can be employed to support complex teaching programs that are personalized to each student according to their learning speed, comprehension, and general capabilities. The system adapts to the progress of the user, offering more nuanced tutoring. Another notable example is Duolingo, a language learning app that leverages LLMs to build courses more efficiently for users.
Customer Support
AI agentic tech is possibly most known in the customer support field. Generative artificial intelligence can power chatbots and virtual assistants, helping customers receive answers to their questions. In cases where chatbots cannot resolve the issue, they aid clients in formulating their requests or inquiries to then transfer them to human specialists.
However, with agentic systems, virtual assistants are capable of drawing unique context from the queries and finding a resolution on their own without involving professionals. For instance, if the customer is facing a problem with their billing, the system can execute the proper steps like verification of transaction history to settle the issue. This ultimately allows specialists to focus on support cases where human examination is irreplaceable.
Future Trends and Opportunities in Agentic AI
As it stands now, agentic technology is still in its earlier stages of development and not as widely adopted as other branches of artificial intelligence. This means the room for growth and improvement in the coming years is extensive, offering more sophisticated and polished features.
Overall, we can predict that the level of automation and unsupervised decision-making will continue developing to require less and less human oversight, enabling companies to fully put trust in such systems to perform designated tasks. Agentic AI companies will deliver customized solutions to businesses of any scale, addressing their specific operational needs.
As for the approaching trends, we can outline several areas where the future of this tech is bound to shine.
- Integrations with IoT devices and robotics
Agentic artificial intelligence can push the envelope in integrations with IoT, powering devices within smart homes and making house management hassle-free. For instance, the system can autonomously adjust the thermostat temperature by recognizing the changes in weather or learning the habits of users in particular situations.
Robotics will also be enriched with this tech, allowing for use cases like self-driving cars to be more spatially aware and make independent rerouting choices. Assembly lines supported by robots will be more vigilant in performing quality checks and detecting defects.
- Smart shopping assistance
AI agentic software is likely to redefine mundane tasks for regular users as well. A bright example of this transformation is Amazon, which strives to adopt AI agents that do the shopping for users. Essentially, such systems could autonomously purchase goods from grocery lists, restock the most-used items in the household, and independently order necessary products for specific recipes the user requests.
- Enhanced multi-agent collaboration
While single-agent systems are already widely used in straightforward scenarios, the collaboration between multiple agents within one ecosystem will continue to advance. MAS will be able to handle enterprise-level operations with minimal bottlenecks or inconsistencies.
An interconnected system will allow one agent to extract valuable information in its domain and transfer it to the following agent, improving synergy and contextual understanding. As a result, the system will be supported by a holistic vision of the underlying processes and take proactive steps, increasing the probability of the best outcome.
- Ethical reasoning and decision-making
The current challenge of agentic AI and other types of artificial intelligence is the lack of high-quality data to train the models on. Datasets can include biased information that can skew the results and lead to unethical decisions that can inadvertently discriminate against certain groups of users.
The solution to this problem is incorporating synthetic data, which is artificially made information that resembles real data without personal details. This will shape the future of agentic systems that will reason without preconceived notions and enable objective decision-making.
These and other trends will define how smart agents progress and the scale of their adoption by corporations.
Conclusion: Unlocking Business Potential with Agentic AI
Progress enabled by artificial intelligence is developing rapidly, offering businesses in diverse sectors new opportunities for growth and productivity. One of the major courses of action with this technology is advancing automation and agency of the underlying processes while supplementing human workers with data-driven insights.
AI agentic tech is at the forefront of this evolution, delivering goal-oriented actions and continuous learning without intervention or supervision. Companies that strive to adopt such systems into their workplace can streamline both critical and mundane tasks to improve business outcomes. In our article, we overviewed what agentic technology is and the role it plays in transforming enterprises, so you can make informed choices.
If you would like to get started on creating an agentic system tailored to your needs, don’t hesitate to contact us. Our team will reach out to discuss our next steps together and bring your project to life.
