May 23, 2025
Listen to Article (13 min)Evolutions in artificial intelligence have created a lot of buzz over the last few years. First, we had simple, rule-based systems, suitable for only a handful of specific tasks. Then generative AI and conversational AI emerged, making AI solutions more intuitive. Now agentic AI is changing the game all over again – emerging as the hottest topic for businesses in 2025.
Agentic AI isn’t just the “next generation” of artificial intelligence. It represents the birth of a new generation of intelligent systems that don’t just answer questions, but anticipate needs, plans tasks, proactively adapts to different situations, and learns from every interaction.
Unlike traditional AI agents, or chatbots, agentic AI models don’t just respond to prompts – they operate with a higher level of autonomy, setting and pursuing specific goals.
For instance, in healthcare, agentic AI is being used to autonomously register participants for clinical trials and ensure proper post-discharge care, reducing administrative burdens on medical staff. In customer service, AI agents are evolving from simple chatbots to sophisticated assistants capable of understanding context and managing intricate workflows, thereby enhancing customer satisfaction.
As the Agentic AI market evolves (growing at a CAGR of over 31.6%), now is the perfect time for businesses and leaders to discover the potential of agentic AI workflows.
Agentic AI refers to a type of artificial intelligence that empowers models to plan, solve problems, make decisions, and operate with a higher level of autonomy. Most agentic AI platforms and tools combine multiple AI technologies, and bots use reasoning and iterative planning to complete tasks.
In simple terms, unlike traditional AI, agentic AI doesn’t just transform data into knowledge, it translates that knowledge into action.
On a broad scale, the main characteristics of agentic AI models are:
Agentic AI startups and innovators often leverage many different technologies used to power different forms of artificial intelligence. In fact, most agentic AI workflows require models to draw on technologies like natural language processing, large language models, machine learning algorithms, and data analysis. The core difference is that agentic AI is more autonomous than its counterparts.
Most other AI solutions available today rely heavily on human input. Even generative AI bots like ChatGPT can only produce content in response to a “prompt”. Agentic AI, on the other hand, is given a goal, and can act autonomously to reach that goal.
These solutions can analyze data, prioritize tasks, allocate resources, and adapt dynamically to new challenges, without constant input from a human.
Since agentic AI is still a relatively new concept for many business leaders, some do struggle to identify the differences between “agentic AI” and “AI agents”. On the surface, these two concepts sound very similar, but they reference very different technologies.
AI agents are typically software programs designed to perform specific tasks based on predefined rules and instructions. They operate within set parameters and are effective for automating repetitive processes. For example, a customer service chatbot that provides scripted responses to common inquiries is a type of AI agent.
These systems are primarily “reactive”, responding to user inputs without the ability to adapt or learn beyond their initial programming. In contrast, agentic AI solutions are autonomous systems capable of self-directed decision-making, learning, and adaptation.
These systems can set and pursue goals, adjust their behavior based on new information, and handle complex, multi-step tasks without human intervention. For instance, an agentic AI in supply chain management could analyze real-time data to optimize logistics, anticipate disruptions, and implement solutions proactively.
| Feature | AI Agents | Agentic AI |
| Definition | Software programs executing that execute predefined tasks according to pre-set rules. | Autonomous systems that can learn, adapt, and complete multi-stage tasks for businesses. |
| Adaptability | Low: AI agents operate within programmed boundaries, and struggle to adapt to complex scenarios. | High: Agentic AI models can update and adapt their behavior based on experience and new data. |
| Decision-Making | Rule-based logic and human intervention. | Self-directed decision-making with minimal human input. |
| Example Use Cases | Chatbots, recommendation systems, process automation. | AI-driven assistants that plan, strategize, and optimize workflows dynamically. |
| Learning Capability | Requires manual retraining for improvements. | Continuously learns and adapts through reinforcement learning. |
As mentioned above, agentic AI platforms and tools leverage a range of fundamental AI technologies and algorithms to operate with a high-degree of autonomy and adaptability. Some of the core technologies involved in agentic AI systems include:
One of the biggest benefits of agentic AI is that it can “learn” from every interaction. Agentic models rely on “reinforcement learning” – a type of machine learning where systems identify “optimal” behaviors based on interactions with their environment and data.
In reinforcement learning, an agent receives feedback in the form of rewards or penalties based on its actions, guiding it toward achieving specific goals. This trial-and-error approach allows the AI to autonomously discover effective strategies without explicit programming for each task.
For instance, Boston Dynamics employs reinforcement learning to enhance their robots’ abilities, enabling them to perform complex movements with minimal human intervention.
Large language models aren’t just for generative and conversational AI tools. They also play an important role in agentic AI architecture. These models give systems the ability to understand and generate human-like text. The models are trained on vast datasets, allowing them to comprehend context, interpret nuances, and generate coherent responses.
In agentic AI systems, LLMs power sophisticated reasoning and decision-making processes by interpreting complex instructions and generating detailed plans. For example, LLMs can be integrated into AI agents to orchestrate the behavior of multiple sub-agents across various applications, enhancing the system’s overall functionality.
Autonomous planning is what allows agentic AI models to complete multi-step tasks in pursuit of a specific goal. It enables systems to set and pursue long-term objections, without having to receive constant, updated prompts from human beings.
For instance, an agentic AI system used for supply chain management could analyze the current conditions of a supply chain, forecast future scenarios, and devise strategies to achieve desired outcomes- all autonomously.
Plus, agentic AI can dynamically adjust to new inputs and evolving environments, making autonomous decisions that align its core goals.
Another hallmark of agentic AI is that these models are excellent at self-improvement. They can constantly refine their processes without external intervention. Through mechanisms like recursive introspection and self-reflection, agentic AI can analyze its performance, identify shortcomings, and adjust its behavior accordingly.
For instance, research has demonstrated that LLMs can enhance their problem-solving abilities by iteratively refining their responses, leading to improved accuracy and efficiency over time.
At this point, you might be saying: “This all sounds impressive, but what are some real-world applications of agentic AI? Which companies are already using this technology?” Examples of agentic AI are actually easier to come by than you’d think.
For instance, agentic AI is helping to power the self-driving cars of tomorrow in the automotive landscape – Telsa’s “Full self-driving” platform is a great example of an agentic AI solution that can adjust its behavior to improve driver efficiency and safety.
In cybersecurity, agentic AI examples come in the form of tools that can detect and respond to vulnerabilities automatically. For instance, Darktrace uses agentic AI to detect, respond to and learn from cyber threats autonomously.
Here are some other great agentic AI use cases, with real-world examples:
Companies have been using AI systems for customer support for years now, leveraging chatbots and conversational agents to handle common queries and processes. Agentic AI takes customer service to the next level, creating bots that can not just answer questions, but complete tasks for clients.
For instance, the AAA Washington team uses agentic AI models to answer customer support requests, automatically keep clients informed about roadside issues, and suggest relevant products to clients. Elsewhere, publisher Wiley, uses agentic AI to improve self-service experiences for customers, helping customers to troubleshoot issues quickly.
In the finance and banking sector, agentic AI is enabling more responsive and intelligent investment strategies. For example, financial institutions like JPMorgan and Bridgewater use agentic AI to develop complex models for investment analysis, improving efficiency and decision-making.
Elsewhere, finance companies are also using agentic AI to improve compliance and tackle fraud. Autonomous agentic AI systems can refine risk assessments, dynamically respond to threats and anomalies, and protect companies against attacks.
In healthcare, agentic AI can support teams with diagnostics, treatment recommendations, and patient management. Companies like Grove AI and Infinitus Systems are developing AI agents to handle administrative responsibilities, thus reducing physician burnout and managing workloads amidst doctor shortages.
Companies like Precina Health are even using agentic AI to improve diabetes care, streamlining everything from patient recruitment, to provider efficiency. Agentic AI even helps the company to deliver more personalized treatment plans to every patient.
In logistics and operations, agentic AI streamlines supply chains by automatically predicting demand, managing inventory, and even altering delivery routes. For example, Amazon’s Warehouse Robots, powered by AI, navigate complex environments, adapt to different conditions, and autonomously move goods around warehouses, ensuring smoother, more efficient operations.
This next-level approach to automation helps companies to significantly reduce operational costs, while improving the speed and reliability of supply chains.
All innovative forms of artificial intelligence can pose risks. Agentic AI, with its robust ability to act and make decisions autonomously, dose present a few challenges. For some companies, even figuring out “how much” autonomy a system should have can be difficult.
Organizations investing in agentic AI use cases will need to be aware of issues like:
At the same time, aligning AI operations with current business processes and regulatory requirements adds layers of complexity. Organizations must carefully plan to minimize disruptions and ensure that the AI systems function as intended, within safe frameworks.
Although integrating agentic AI into workflows can be complex, some innovators are emerging to help businesses embrace the benefits of this technology, while minimizing risks. For instance, Voicing.AI, a cutting-edge, AI-driven voice automation platform, is harnessing the power of agentic AI to enhance customer service and enterprise applications.
By integrating proprietary Large Language Models (LLMs) tailored to specific industries, Voicing.ai ensures domain-specific expertise and highly accurate conversational responses. Voicing AI delivers:
What sets Voicing.AI apart from other agentic AI startups and vendors, is its focus on ethics, security, compliance, and enterprise-grade adaptability. Voicing.AI empowers companies to develop their own guardrails for agentic solutions, leverage integrations between ecosystems, and even tap into AI tools with human-level emotional intelligence.
Unlike traditional AI chatbots that may rely on scripted responses, Voicing.AI’s agents understand context, detect emotions, and execute tasks without manual intervention. This results in more natural and effective interactions, aligning closely with customer expectations and business objectives.
Voicing.AI’s agentic AI solutions can serve a range of use cases, including:
Agentic AI represents a massive step forward in the artificial intelligence space. It’s not just about making AI systems more powerful – it’s about enabling them to operate with autonomy, adaptability, and goal-oriented behavior.
Unlike traditional AI, which relies on predefined rules, agentic AI possesses the ability to learn, reason, and make decisions independently, transforming all kinds of business processes. On a broad scale, agentic AI could change the world as we know it, introducing a new era of innovation, efficiency, and productivity across sectors.
Voicing.AI helps companies to embrace the benefits of agentic AI, while minimizing risks. With this enterprise-grade platform, organizations can design customized, secure, and compliant agentic AI tools that follow specific guardrails and integrate with their existing systems.
If you’re ready to join the agentic AI revolution, contact Voicing.AI today.
