What is Agentic AI?

Understanding Agentic AI

At its core, agentic AI consists of machine learning models (AI agents) that mimic human decision-making to solve problems in real time. In a multi-agent system, each agent handles a specific subtask, and their efforts are coordinated through AI orchestration to achieve broader objectives.

Unlike traditional AI, which operates within predefined constraints and often requires step-by-step guidance, agentic AI exhibits autonomy, goal-driven behavior, and adaptability. The term “agentic” reflects these systems’ capacity to act independently and purposefully. Rather than simply reacting to inputs or following preset rules, agentic AI proactively performs complex tasks and adapts to dynamic environments without constant human oversight. In essence, agentic AI represents the next evolution of artificial intelligence: autonomous, adaptive, and capable of independent, goal-driven action.

Types of Agentic AI Systems

Agentic AI can be structured as either a single-agent system or a multi-agent system.

• Single-Agent System: In a single-agent AI setup, one AI agent manages all tasks in sequence. This approach is ideal for businesses that require quick solutions for clearly defined problems or standardized processes, as it simplifies coordination and decision-making.
• Multi-Agent AI System: In contrast, multi-agent AI systems involve multiple AI agents working together, each focusing on a portion of a larger workflow. This setup is highly scalable and well-suited for handling complex, dynamic scenarios. Most modern agentic AI deployments adopt this multi-agent approach due to its flexibility and capacity to tackle intricate problems that a single agent cannot efficiently manage.

Structures of Multi-Agent Systems

• Horizontal Multi-Agent Systems: In horizontal systems, all AI agents operate at the same technical level and have similar capabilities. Each agent specializes in a specific task or skill, and the results from all agents are integrated to solve larger, more complex problems. Collaboration and communication between agents are essential in this setup, allowing the system to leverage specialized knowledge across multiple domains.
• Vertical Multi-Agent Systems: Vertical multi-agent systems are organized hierarchically. Lower-level agents handle simpler, routine tasks such as data collection, formatting, or preprocessing, while higher-level agents are responsible for more advanced functions, including critical thinking, reasoning, and complex decision-making. This hierarchical structure allows the system to efficiently process large amounts of information, passing refined data upward to the higher-level agents for analysis and action.

By combining these structures, multi-agent AI systems can distribute workloads effectively, increase processing efficiency, and adapt to increasingly complex environments, making them ideal for enterprise-scale applications and sophisticated problem-solving.

How Agentic AI Works

Agentic AI functions through a structured cycle of four core stages: perceive, reason, act, and learn. Each stage draws on advanced AI methods and technologies to enable autonomy, adaptability, and continuous improvement.

Perceive

In this first stage, AI agents collect and interpret data from a wide range of sources, including sensors, APIs, databases, and digital platforms. The goal is to extract meaningful information such as identifying objects, recognizing patterns, or highlighting key entities in the environment that can inform decision making.

Reason

Once the data is processed, reasoning comes into play. Here, a large language model often serves as the “brain” of the system, interpreting tasks, generating solutions, and orchestrating other specialized models. For example, different sub-models may handle natural language content creation, visual processing, or personalized recommendations. Techniques like retrieval-augmented generation allow the system to tap into proprietary or external knowledge sources, producing outputs that are accurate, contextual, and up-to-date.

Act

During the act stage, agentic AI transitions from planning to execution, carrying out the strategies formed in the reasoning phase by integrating with external tools, workflows, and software systems through APIs. These connections allow the AI to not only communicate with external platforms but also run tasks inside them, making it capable of driving meaningful outcomes across different applications.

The actions themselves are often composed of multiple subtasks, which the AI agent organizes and executes in sequence. Depending on the use case, these tasks might include compiling code, updating or migrating applications, simulating processes, analyzing and editing documents, or performing functions inside enterprise software tools.

Learn

The final stage is all about adaptation and continuous improvement. Agentic AI relies on a feedback loop, sometimes referred to as a “data flywheel”, where the results of its actions generate new data that feeds back into the system. This process sharpens models, improves accuracy, and strengthens decision-making over time. Through techniques like reinforcement learning, AI agents refine their actions based on outcomes, whether guided by autonomous agents, large language models, or human feedback. Performance is tracked using metrics such as latency, confidence, and success rate.

In multi-agent setups, learning is distributed. Agents share insights through communal memory layers, allowing the entire system to benefit. This continuous cycle of feedback and refinement enables agentic AI to thrive in dynamic environments, scaling automation while driving efficiency and adaptability.

Agentic AI Benefits

Agentic AI is unlocking a new era of automation, enabling advanced AI agents to tackle a broader range of complex challenges than traditional systems ever could. For businesses, this evolution translates into several key advantages:

• Human Augmentation: Human augmentation through agentic AI allows these systems to work alongside humans as collaborative partners, enhancing and complementing rather than replacing human capabilities. By managing complex decision-making pathways, agentic AI reduces cognitive load and frees individuals from the burden of tedious or highly detailed time-consuming tasks that demand significant human effort. At the same time, it boosts productivity by delivering context-aware insights, intelligent recommendations, and automated support, enabling employees to focus on creativity, strategy, and problem-solving that drives long-term growth.
• Enhanced Customer Relationships: With its ability to adapt interactions in real time, agentic AI delivers more personalized customer experiences. Drawing on existing data, it can tailor recommendations, adjust tone and style to match user preferences, and respond with contextually relevant solutions. Beyond direct engagement, agentic AI can sift through customer feedback, highlight recurring themes for product teams, and even respond directly to users, strengthening trust and demonstrating that customer voices matter.
• Ongoing Optimization: Unlike static automation tools, agentic AI learns continuously. By leveraging feedback loops and adaptive learning methods, it refines its performance with every task, becoming smarter and more efficient over time. This ensures businesses see compounding benefits as the system evolves.
• Scalability and Flexibility: Agentic AI offers scalability and flexibility by adapting to a wide range of business needs and complex environments. Through multi-agent systems, large and challenging tasks can be broken down into smaller, coordinated actions, making it easier to expand automation across an entire enterprise. This approach enables organizations to handle both structured and unstructured processes effectively, ensuring that automation remains robust and versatile as demands evolve.

Agentic AI Risks and Challenges

Developing and deploying agentic AI comes with a unique set of challenges that businesses must carefully navigate to ensure reliability and success.

• System Design: Designing a robust multi-agent architecture is a very complex hurdle. These systems must not only coordinate seamlessly with other agents but also possess domain-specific expertise, the ability to perform advanced reasoning, and the capacity for long-term strategic planning. Because agentic AI is still an emerging field that draws on a variety of advanced AI techniques, building effective systems requires significant technical expertise. Many organizations may find it difficult to implement a fully functional agentic AI solution without substantial resources and specialized knowledge.
• Testing and Debugging: Another difficulty lies in testing and debugging systems that operate with high levels of autonomy. Since agentic AI often functions with minimal human oversight, pinpointing where and why an error occurs can be complicated. To mitigate this, developers must embed mechanisms for traceability and reproducibility, ensuring that mistakes can be tracked, analyzed, and corrected. Without this, it becomes nearly impossible to refine the system or maintain reliability.
• Trust and Transparency: Finally, establishing trust and transparency is critical. Agentic AI, like other advanced systems, can generate incorrect outputs. If inaccurate information is passed between agents, errors can cascade through the workflow and severely impact results. This risk is especially concerning in high-stakes industries such as finance, law, or healthcare, where incorrect data could have major consequences. To build confidence, organizations must implement safeguards that validate outputs and ensure transparency before deploying agentic AI at scale.

Agentic AI Examples and Use Cases

Agentic AI is being applied across industries in ways that go far beyond the capabilities of traditional AI systems. Below are some of the most impactful use cases and real-world examples of how businesses and organizations are applying agentic AI today.

• Customer Service: One of the most common use cases is customer service, where agentic AI functions as a more advanced alternative to rule-based chatbots. These systems can manage complete conversations, access knowledge bases, and even escalate complex cases to human agents when necessary. For example, an insurance provider might use an agentic AI assistant to automatically process routine claims, while ensuring higher-value cases are reviewed by human adjusters. This not only speeds up service, but also builds customer trust through consistent and personalized support.
• Healthcare: Healthcare is another field where agentic AI has significant impact. For physicians who must navigate large volumes of patient records and medical data, agentic AI can act as an intelligent assistant, extracting key insights that support more accurate and timely clinical decisions. By handling routine administrative work such as documenting visits, capturing clinical notes, and updating health records, these systems free doctors from repetitive tasks, giving them more time to focus on meaningful interactions with their patients. This layered collaboration allows healthcare providers to deliver faster, more precise diagnoses and reduce administrative burdens. Beyond supporting practitioners, AI agents also enhance the patient experience. They can provide around-the-clock assistance, answering common questions about prescriptions, managing appointment scheduling, sending reminders, and guiding patients through treatment plans. This constant availability helps improve adherence to care, while ensuring patients feel supported outside of office visits.
• Finance: In the financial sector, agentic AI is being used for fraud detection, risk management, and automating complex workflows. A multi-agent setup might include one system monitoring real-time market conditions, another assessing portfolio risks, and a third executing trades in alignment with compliance requirements. The adaptability of these systems improves accuracy, strengthens fraud detection, and makes it possible to scale investment strategies more effectively than manual processes.
• Supply Chain and Logistics Management: Supply chain and logistics management have also been transformed by agentic AI. By breaking down complex workflows into smaller, coordinated tasks, these systems can track shipments, anticipate delays, optimize delivery routes, and update customers in real time. This enhances efficiency, strengthens resilience against disruptions, and reduces costs caused by inefficiencies in global supply networks.
• Cybersecurity: In the field of cybersecurity, agentic AI can serve as an always-on-defense system by continuously analyzing network traffic, scanning system logs, and tracking user activity for unusual patterns. These intelligent agents can flag irregularities that may signal potential threats such as malware infections, phishing attempts, or unauthorized access. Beyond simply detecting issues, they can also prioritize alerts, isolate compromised devices, and even initiate automated countermeasures in real time. By learning from past incidents, the system becomes more adept at recognizing emerging threats, helping organizations stay ahead of increasingly sophisticated cyberattacks.

In all these examples, agentic AI proves its value by operating not just as a tool but as a collaborative partner. Its ability to perceive, reason, act, and learn allows it to adapt to new challenges, scale across business functions, and deliver sustained improvements in efficiency and innovation. As adoption grows, organizations that embrace these systems will gain a significant advantage in handling complex tasks and unlocking new opportunities for growth.

The Future of Agentic AI

The rapid advancements in artificial intelligence have sparked a profound technological transformation. At the forefront of this change is agentic AI; systems designed not just to process information, but to learn, adapt, and take meaningful action. Unlike earlier generations of AI that were limited to narrow, predefined tasks, agentic AI agents can tackle open-ended challenges, oversee intricate workflows, and make context-aware decisions that were once the sole domain of humans.

This shift is unlocking entirely new approaches to how organizations design and execute work. Agentic AI extends automation far beyond repetitive processes, opening the door to reimagining the relationship between people, machines, and intelligent systems across the enterprise.

What we are seeing today is only the beginning. The integration of agentic AI is setting the stage for a dramatic redefinition of the modern workplace. In the years to come, the division of labor between humans and AI agents will look radically different from what we know today, with new models of collaboration emerging.

As these systems scale and use cases proliferate, businesses will gain unprecedented agility and efficiency. Organizations that embrace agentic AI will distinguish themselves through exceptional customer experiences, faster responses to shifting market demands, and a more engaged, empowered workforce.

The horizon for agentic AI is wide open. Its evolution signals not only a new era of productivity but also a reshaping of human and machine roles in ways that will transform industries and redefine the future of work.

Unlocking the Power of Agentic AI with Reltio

Agentic AI has the potential to fundamentally reshape how enterprises operate, driving automation, adaptability, and intelligence at an unprecedented scale. However, the effectiveness of these AI agents is only as strong as the data that powers them making quality, trust, and governance non-negotiable.

That is where the Reltio Data Cloud and its Master Data Management (MDM) solution provide a critical advantage. By delivering real-time, clean, connected, and governed data across every domain such as customers, products, suppliers, and more, Reltio ensures that agentic AI systems are working from a single, trusted source of truth. This foundation allows AI agents not only to make accurate and timely decisions but also to coordinate complex processes, adapt dynamically to changing conditions, and integrate seamlessly into enterprise workflows.

Together, Reltio and agentic AI empower organizations to scale automation across silos, reduce inefficiencies, elevate customer experiences, and accelerate innovation. In today’s rapidly evolving digital economy, this powerful combination equips enterprises to move beyond incremental improvements and lead the next wave of data-driven transformation.