Today, artificial intelligence (AI) is everywhere—it’s successfully integrated into healthcare, HR, content creation, and everything beyond. This technology is here to stay, and more companies are embracing this shift.
The numbers for AI exceed almost every other technology out there: the market size for AI in 2025 is projected to grow to $243.72 billion, with a CAGR of nearly 28% from 2025 to 2030. It’s probably unsurprising, then, that AI doesn’t stay in place and continues developing. While earlier models were relatively limited and required significant human input, today’s AI is adaptable and leverages deep learning and neural networks to grow more creative and solve diverse challenges.
Now, a new step in AI evolution is here—agentic AI. Not limited by the constraints of traditional AI, agentic AI operates with autonomy, continuously learning, making decisions, and taking actions with minimal human intervention.
The global market demand for agentic AI tools will jump: In 2024, its value was around $5.1 billion, but in 2030, it’s expected to reach $47 billion. By combining the new technological freedom and ability to learn with the experiences of the previous models, agentic AI will revolutionize all industries.
This article will discuss agentic AI definition and agentic AI vs. traditional AI, provide agentic AI use cases, and answer the question “What are the potential risks associated with agentic AI?”
What is agentic AI?
Let’s start with the easy stuff: what does agentic AI mean? You are already familiar with what AI does—it usually analyzes data and generates content based on the input. However, agentic AI is different. Agentic AI is a system that operates with a high level of autonomy and primarily targets problem-solving, decision-making, and developing new solutions to complex challenges.
The key features of agentic AI are:
- adaptability;
- autonomy in decision-making;
- reinforcement learning;
- usage of large language models (LLMs), machine learning (ML), and natural language processing (NLP);
- better contextual understanding;
- limited human involvement and
- high level of integration with external systems.
Now that this is clear, how does agentic AI differ from traditional AI systems?
Traditional AI, already effective enough, significantly relies on input to continue making decisions and growing. However, agentic AI is proactive and uses goal-oriented reasoning to set specific tasks and adjust based on the context of the situation. This leads to another difference: traditional AI is limited by specific scenarios, while agentic AI understands context and changes its behavior in real time.
How these types of AI handle tasks also differs: the traditional model is more single-task-focused. In contrast, the agentic one can handle multi-level issues and involve complex problem-solving. Both AIs can learn, but traditional AI uses mostly past situations to refine its further actions—it’s different for agentic AI that continuously refines strategies and can predict situations that haven’t yet occurred.
Such a shift illustrates the rapid advancement of how machines interact with the world and their role in improving people’s lives.
Agentic AI use cases in different industries
What industries are most likely to benefit from agentic AI? From self-driving vehicles to intelligent customer service assistants, these agentic AI solutions assist humans in the most demanding environments.
Agentic AI in transportation
Most people can easily envision agentic AI in transportation because it’s been around for some time. You’ve heard about autonomous vehicles: agentic AI enables them to actively perceive their surroundings (traffic, road situation, weather conditions, etc.) and make split-second decisions.
These AI-driven systems process real-time sensor data from cameras, LiDAR, and radar to detect and understand the physical environment, immediately updating if something changes.
The autonomous vehicle market share in 2022 was almost $105 billion and is expected to reach nearly $615 billion in 2030. With agentic AI, traditional self-driving vehicles don’t just rely on rules but continuously adapt to new driving conditions, which improves safety on the streets.
Agentic AI in healthcare
Have you heard of the robots or programs that help doctors perform operations or diagnose illnesses? That’s how helpful agentic AI in healthcare can be, and the opportunities are growing every day. Agentic AI enhances diagnostics by analyzing vast medical datasets, detecting patterns that human doctors might miss, and continuously refining its accuracy.
Just last year’s research publication has found that AI has outstanding accuracy rates, even achieving up to 92.5% performance success in some risk assessments for different health conditions.
In surgeries, AI assistants can perform the precise movements that can be too difficult for human agents due to simple limitations of their bodies. For example, mistakes from overworking, stress, or even shaking hands can be finally minimized.
Agentic AI in finance
The finance market, from trading to banking, is lush for AI integrations—this technology allows companies and individuals to make smarter decisions and increase their wealth. Even more so, they grow increasingly helpful for fraud prevention and security measures.
Unlike rule-based fraud detection systems, agentic AI learns from evolving fraud patterns, adapting its detection methods in real time to counter emerging threats. Trading, on the other hand, gets easier as this technology makes decisions based on data-driven strategies.
Statistics reveal that AI in finance is actively used today: The AI market size in fintech is $42.83 billion worldwide, and as it gets easier to integrate, it will take up more space and offer new solutions.
Agentic AI in manufacturing and supply chain management
There are plenty of opportunities for agentic AI in manufacturing: certain areas require better performance by adjusting workflow, predicting equipment failures, and optimizing resource allocation.
While the data on agentic AI remains limited due to how novel this AI branch is, there is valuable information on how this technology is generally used. In 2020, quality control had the most use cases for it (59%) in the manufacturing industry, with inventory management (44%) and monitoring/diagnostics (32%) following it.
In the supply chain, agentic AI can assist professionals with inventory tracking and demand forecasting, reducing expenditures on potential prediction errors.
Agentic AI in logistics
Logistics is one of the industries where the opportunities of agentic AI can be useful on all levels. In 2024, AI in logistics reached almost $18 billion and is expected to grow to $26.35 billion.
With agentic AI, this technology can help with smart routing and 24/7 shipment tracking. As a result, companies can minimize losses due to delays or wrong delivery. In warehouses, autonomous robots can manage inventory and sort packages.
Smart cities and infrastructure
Urban environments are experiencing a rapid transformation, with traditional views on how cities should look becoming redundant. Today, urban planning is more than just creating skyscrapers—it’s about making green areas and inclusive spaces for all.
Agentic AI can contribute to this by improving energy consumption, planning building allocation, and enhancing public safety, cleanliness of air/water, and more. As cities become more connected as systems, they can provide information about traffic, accidents, or even infrastructure maintenance needs.
Customer service
Customer service already readily utilizes AI to handle routine tasks and respond to repetitive inquiries. In 2024, the most popular use cases of AI in contact centers were voice authentication and process automation (both 37%).
However, as agentic AI can handle more complex tasks, customer service will likely provide more services and opportunities. It will handle complex interactions or predict customer behavior during interactions. This change will shift repetitive and (sometimes) frustrating interactions toward more natural and practical solutions.
These agentic AI applications reveal how diverse they can be. However, what industries are currently benefiting the most from agentic AI? Healthcare, logistics, and manufacturing are the most likely answers.
Examples of agentic AI in action
For those still asking, “How does agentic AI differ from traditional AI?” the case studies below can provide impressive examples of the newest technology adaptation.
Ema—agentic AI in customer support
Ema brings the latest experiences of agentic AI into its tools. It offers an AI chatbot, agent assistant, agent QA, and knowledge base assistant. This agentic AI example claims to resolve up to 70% of customer issues with its AI bot and save over 80% of agent time with its agent assistant. It’s an adaptable service that moves from responding to inputs and can be used as a tool for collaboration with consultants.
Juna.ai—an agentic process control platform
Juna.ai is a platform that uses agentic AI to control its clients’ industrial processes. Among all agentic AI examples in this list, it takes the top place for innovation. It tracks plant processes, optimizes production, and reduces energy consumption. It follows all the plant procedures and generates reports on demand. Its real-time updates with continuous improvement suggestions rely on agentic AI’s novel tech opportunities.
Atomwise—agentic AI in drug discovery
Atomwise uses agentic AI to discover drugs with the help of its technology, AtomNet. With contextual adaptation and fast learning, it studies chemical datasets to predict molecular behavior and potential drug performance. The company compares its technology to image recognition. The pharma industry currently taps into AI-powered molecular simulations and data analysis to develop new drugs or discover their inefficiencies.
Waymo—self-driving cars
Waymo has developed Waymo Driver, which leverages the latest agentic AI to offer a completely hands-off experience for passengers. You have probably heard of similar examples of agentic AI, but many of them aren’t really the same and rely on humans for control.
According to its website, Waymo emphasizes safety and has “millions of miles driven on public roads and billions of miles in simulation,” which has enabled the company to make an independent driver. It maps out the environment and keeps track of external and internal factors affecting the drive.
These agentic AI startups demonstrate how to respond to the already existing need and turn it into something unique.
Challenges and risks of agentic AI
Despite the undeniable potential of agentic AI, it’s not immune to threats and challenges, especially since it’s so new. Businesses planning to adopt it and asking themselves, “How to create agentic AI?” should be aware of the risks to ensure they know how to address them beforehand. So, the question you should ask instead is, “What are the main challenges in implementing agentic AI?”
Ethical concerns and bias
With AI, there’s always a danger of bias due to the datasets it’s trained on. If not properly addressed, these biases can lead to unfair outcomes in hiring or law enforcement, for instance, where they can hurt vulnerable populations.
What are the ethical considerations associated with agentic AI? It grows more autonomous and no longer requires human input—this will create questions of whether technology should be so freely making decisions that affect humans without much control.
Predictability and transparency
Unlike traditional AI, agentic AI dynamically learns and adapts, leading to a less predictable decision-making process. Such a fast agentic AI development and improvement makes it more difficult to control. This technology has not been studied entirely.
Many businesses still ask, “How does agentic AI work?” only after implementing it, which can cause problematic outcomes. It’s still not entirely clear how these AI technologies will perform in high-stakes situations, especially in healthcare, politics, finance, or criminal justice.
Plus, the lack of transparency makes AI auditing more complex, leading to a particular limitation of innovation management.
Security risks
Agentic AI automation can be a target for security threats as it starts to handle sensitive and financially valuable data. If it’s accessed, it can wreak havoc on the business it attacks. There’s also a risk of error—even the best technologies can fail, which can lead to catastrophic results if they control critical infrastructure or financial markets.
Cybercriminals can also use the same technologies to attack private or public resources or integrate into the areas we have highlighted above.
Shaping tomorrow
The groundbreaking shift with agentic AI we are witnessing will revolutionize how we perceive the integration between humans and technology. Businesses that want to stay competitive should leverage it to optimize their performance and enhance problem-solving before they get left behind.
Need help determining how to build agentic AI or integrate it into your operations? Contact us. We’ve got experience with the latest industry trends and know how to make a unique product for your budget and vision.
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