Collaboration among agents further amplifies their power. Multiple AI agents can interact to solve larger, more complex problems without continuous human supervision. Within such systems, agents exchange data to achieve common goals. Specialized AI agents perform subtasks with high accuracy, while an orchestrator agent coordinates their activities to complete broader, more intricate assignments. This coordinated approach is more powerful, leveraging the unique capabilities of different AI models, producing results that exceed what individual agents could achieve alone.
What is agentic AI?
Agentic AI refers to artificial intelligence systems that can achieve a larger objective with a high degree of autonomy. These systems use a suite of tools (like LLMs, external applications, and APIs) to gather information, reason through complex problems, interpret and create actionable plans, and execute them. In a multi-agent system, each AI agent is assigned a specific subtask, and their efforts are coordinated through orchestration mechanisms.
The term ‘agentic’ in this context refers to models’ agency or power to initiate actions and make choices independently and purposefully—unlike traditional passive AI systems that operate within predefined constraints, require direct human intervention, and only respond to commands. Key features of agentic AI include autonomy, goal-driven behavior, and adaptability.
Generative AI provides the foundation upon which agentic AI is built, enabling the reasoning and generative capabilities that agents use to plan and act. While generative AI models, particularly LLMs, create novel content based on the patterns in training data, agentic AI goes a step further by applying this content to accomplish real-world tasks. For instance, a generative AI system might generate code and stop there. An agentic AI system, however, could generate the code, run it, monitor for errors, store the results in a file, and call external tools (programs, APIs, or services) to ensure the objective is met.
Consider a practical example: A generative AI model might suggest the best time to visit Switzerland for a family vacation based on your work schedule. An agentic AI system, on the other hand, could search for flights, check hotel availability, make reservations, and send confirmations—managing the end-to-end task autonomously.
Agentic AI is built from complex autonomous software components known as agents, which use large amounts of training data and learn from user behavior to improve over time. Each agent is unique in abilities and is designed for specific tasks, operating like members of a team to solve bigger, more complex problems. This advanced framework leverages a combination of cutting-edge technologies such as machine learning, NLP, and knowledge representation, enabling agents to learn, adapt, interact, and make informed decisions.
The underlying architecture of agentic AI spreads across multiple computers or servers, connected via a network for scalability and performance. This system enables multiple agents to operate simultaneously across different servers within a shared platform. Agents coordinate and communicate seamlessly in real-time to work together on a problem. This architecture ensures that the system remains fast, reliable, and adaptable to meet evolving demands. Each AI agent is independent and a complete unit, capable of autonomously completing tasks and managing workflows by leveraging machine learning, algorithms, and predictive analytics to make real-time decisions.
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Agentic AI relies on several foundational functions to solve complex problems:
Perception: Agentic AI begins by collecting data through APIs, sensors, or user interactions and turns it into insights. They pinpoint meaningful patterns and make inferences from their environment. This ensures that the system is fed with the latest information to analyze and act.Reasoning: Once data is collected, the agentic AI interprets it using technologies, such as NLP, computer vision, and knowledge graphs. This enables agents to understand user intent, uncover relationships within the data, and grasp the broader context. Such reasoning enables AI to perform tasks, such as answering question, generating recommendations, or alerting humans to potential issues.Goal setting: This step involves defining objectives based on user inputs. The AI formulates a plan or strategy to reach that goal using advanced techniques, such as decision trees, reinforcement learning, and planning algorithms, like A* search (finds the most efficient path to a goal, or genetic algorithms).Decision-making: The system evaluates a variety of possible actions, weighing trade-offs based on efficiency, accuracy and predicted outcomes. It leverages advanced mathematical and statistical models to make intelligent and purposeful decisions.Action: AI agents take their chosen course of action and perform the necessary operations, either by connecting with external systems (APIs, databases, and robotics) or by engaging in a conversation with users through natural language.Learning and adaption: Agentic systems improve over time by learning from feedback. Reinforcement learning and supervised learning technologies are used to fine-tune its strategies over time, enhancing its decision-making capabilities in handling similar tasks in the future.
Agentic AI systems and orchestrations
AI orchestration refers to the automated coordination and management of models, services, and data. It ensures that AI systems and agents work together smoothly toward a common goal. Orchestration automates AI workflows, monitors data flow and memory, tracks progress toward task completion, and adjusts dynamically if something goes wrong. The sophisticated architecture enables numerous agents to work together in harmony. Orchestration streamlines the end-to-end lifecycle and delivers greater efficiency, responsiveness, and scalability.
An agentic AI system is a coordinated system where multiple AI agents collaborate to solve complex problems. While a single AI agent represents a one individual entity with its own built-in capabilities or a suite of tools, an agentic system relies on orchestration to connect and coordinate those agents with other models, external tools and data sources to work together seamlessly. Each agent in multiagent systems could have its own goals, tools, and specialized capabilities, yet they work together seamlessly to tackle multi-layered challenges.
Types of AI agents in multi-agent systems
During the implementation and orchestration of multi-agent systems, users interact with three types of AI agents:
Principal agent: Also known as the manager agent, this agent is responsible for understanding the user’s objectives and coordinating efforts to achieve the desired outcome. It dynamically creates a plan, then delegates tasks to other agents, and ensures the overall project stays on track.Service agents: These are specialized agents equipped with domain-specific knowledge and tools to perform specific tasks. They receive instructions from the principal agent and handle a specific component of the larger plan.Task agents: These are micro-operators designed to execute very specific, granular actions—such as reading a file or making a single API call. They operate without awareness of the broader plan and perform exactly as instructed by a service or principal agent.
A multi-agent system integrates agents of varying complexity, from simple ones (that follow predefined rules) confined to strict boundaries to sophisticated ones (that plan and reason to achieve a goal). Their interactions with one another, with tools, or with users depend on the design of the system.
The operational mechanism of agentic AI is designed to drive autonomy, adaptability, and scalability. By leveraging advanced technologies, collaborative orchestration, and open-source frameworks, agentic AI holds the potential to transform various industries and roles, ultimately improving human-technology interaction.
Industry applications of agentic AI
Many sectors are exploring agentic AI for its potential to transform operations. Its ability to handle tasks involving high complexity, routine data processing, and time-critical decision-making is driving rapid adoption across industries. Common use cases include:
Healthcare and life sciences: Agentic AI can analyze vast amounts of medical data and automate routine tasks. AI agents function like digital assistants for healthcare professionals, monitoring patient vitals, reviewing medical histories, recommending treatment adjustments or alerting medical staff to critical issues. They also manage routine tasks such as summarizing patient records, capturing visit notes, scheduling appointments, and responding to medical-related queries. By automating administrative tasks, they free up clinicians to focus on direct patient care.
Multimodal agents integrate diverse inputs such as medical images, audio, and text together. For example, they can analyze x-rays (images) and patient charts (notes, lab results) simultaneously to assist diagnosis. Beyond hospitals, AI agents are applied across biotech and drug research and development tasks, managing lab data, personalizing treatment plans, and simulating experiments.Finance and business: Banks and other financial institutions use agentic systems to automate financial services, such as lending and trading. Compared to the rigid, rule-based conventional lending system, agentic AI ingests real-time data—from borrower behavior and macroeconomic trends to regulatory changes— to make complex credit decisions, such as setting interest rates, designing personalized loan packages, and flagging anomalies, with minimal human oversight. This accelerates cycles and reduces costs.
Trading platforms also use AI agents to process live price feeds, news, and market signals, executing trades continuously to maximize profitability.Customer service and marketing: AI agents are evolving from simple chatbots into proactive digital colleagues in customer support and marketing. For example, chatbots not only answer FAQs but also resolve issues. They can access customer accounts, troubleshoot billing issues, book service appointments, and even engage in a back-and-forth dialogue to find a resolution, and escalate complex cases to human agents.
Similarly, marketing teams deploy agentic systems to analyze consumer behavior and generate personalized content at scale. In e-commerce, agents embedded in websites track browsing patterns and purchasing signals to recommend products, optimize upselling, and personalize customer journeys.Software, data and IT operations: Agentic AI can automate repetitive coding tasks, boosting developer productivity. A McKinsey research suggests AI could automate up to 30% of routine work hours by 2030. Enterprises also deploy AI agents to streamline a wide range of complex IT and support tasks. Agents can categorize the ticket, prioritize it based on urgency, and route to the correct human agent who can solve the problem.
Agents can also be deployed to manage the setup and configuration of a company’s cloud-based computer systems, as well as perform ongoing maintenance to keep everything running smoothly. In many cases, they can resolve issues directly without any human intervention. In short, agents can automate and streamline vast IT workloads (from code refactoring to database queries) by leveraging APIs, tools, and documentation with minimal human direction.Logistics and supply chain: Agentic AI can optimize a complex supply chain by integrating internal data (inventory, order management) with external signals (weather, shipping updates, demand signals) to autonomously coordinate and manage all the different components of a larger process. This continuously forecasts demand and plans various parts of the supply chain, for example, shipments, inventory allocation between warehouses to meet service goals. Agents can dynamically identify risks (delays/ disruption), replan transport routes, reallocate stock, and negotiate with carriers, leading to improved service levels, reduced logistics costs, and lower emissions.Autonomous systems: Agentic AI powers self-driving cars, drones, and robots by gathering, processing, and analyzing, real-time sensor data (camera, lidar, GPS) and external information (traffic, weather) not only to plan the route but, more importantly, to respond and adapt to unexpected and unpredictable events in its environment, such as a sudden obstacle in the road. Research suggests agentic AI in transportation can optimize routing to cut operational costs by up to 15% while improving service delivery. Similarly, in aviation and shipping, agentic systems use real-time sensor data from aircraft and vessels to schedule maintenance and autonomously handle disruptions—improving safety, efficiency, and reliability.
It is clear from the above use cases that agentic AI is cross-domain—ranging from healthcare, finance, to customer service and retail. Autonomous agents can learn from domain data and execute with human-like competence. Companies across industries are already reporting substantial efficiency gains. For example, McKinsey’s research suggests an agentic system can improve productivity up to 40% in many industries, driving innovation and reducing costs when applied correctly.
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The adoption of agentic AI systems across industries introduces a novel risk landscape that is different from the traditional AI and automation risks. These sophisticated AI systems, known for their ability to operate with increasing degrees of autonomy and solve complex problems, pose distinct challenges and amplify existing ones— necessitating careful consideration and customized risk management strategies.
The self-adaptive nature of agentic systems fundamentally changes how risk management is approached. By identifying critical points where these risks manifest and putting guardrails in place, businesses can take benefits of agentic AI without losing sight of safety and compliance. Successful implementations of agentic systems are a fundamentally different technology paradigm, requiring updated governance and controls.
Agentic AI systems: Risk and key mitigations
Goal misalignment
One of the fundamental risks of adopting the agentic AI systems is that they might not stay fully aligned with the organization’s actual automation goals. Some risk of misalignment already exists in self-learning or self-calibrating models. Agentic AI systems may amplify this risk by far as they operate with greater autonomy in dynamic environments. Over time, objectives might drift away from the organization’s true objectives.
Because agentic AI systems create plans and act to achieve goals, they introduce new risks related to how they interpret situations and pursue objectives. This can lead to behaviors misaligned with human values, as well as legal and ethical considerations. For example, a healthcare scheduling agent might start overbooking patient appointments to maximize efficiency and reduce idle time, unintentionally compromising the quality of care and disregarding doctors’ availability and patient needs.
Risk mitigation
Reasoning & Planning Layer Key Controls:
Explicit goal specification: Provide clear, specific, measurable, and comprehensive instructions to define the agent’s objectives, ensuring alignment with business goals as well as regulatory and ethical standards.Mandatory guardrails: Establish rules and dynamic mechanisms that clearly define what the agent is permitted and prohibited from doing in order to achieve the intended objectives.Value-aligned learning and monitoring: Implement mechanisms that enable agents to continually learn and refine their understanding of human values and organizational standards through fine-tuning and feedback. Additionally, ensure real-time monitoring of their behavior, goal adherence, and performance.
These controls help ensure that agentic systems are optimized for objectives aligned with the company’s priorities, values, intent, and regulatory standards, while preventing them from interpreting goals on their own or pursuing unintended objectives that could cause financial or reputational damage.
Autonomous action
Agentic AI systems can operate autonomously without human approval to perform each subtask, sometimes potentially yielding some unintended results. Agents can interact with real-world systems and make independent, sequential decisions based on outputs. The autonomous nature of agentic AI complicates real-time human intervention, creating regulatory, ethical, and operational challenges, particularly in assigning accountability for harm (e.g., a medical AI producing a wrong diagnosis, or a military drone misidentifying a target) when humans are absent from the decision chain.
Risk mitigation
Reasoning & planning layer and tools – key controls
Action scope limitations: Define precise boundaries to limit the agent’s independent operation. Implement granular permissions that specify where and when the agent is allowed to act, placing clear limits on tool access to ensure alignment with intended purposes.Human in-the-loop thresholds: Establish well-defined thresholds requiring human review and approval before execution, with criteria based on risk exposure and materiality.Graduated autonomy framework: Start with a low level of autonomy and scale up incrementally only when the agent consistently meets or exceeds predefined performance, safety, and quality benchmarks.Comprehensive logging and audit trails: Maintain detailed, step-by-step records of the agent’s decision-making process to support post-mortem analysis, accountability, and regulatory compliance.Continuous agent behavior monitoring: Track predefined KPIs and operational metrics (e.g., task execution, instruction adherence, number of steps taken). This creates a real-time safety net that helps detect and prevent potential issues before they escalate.
Agentic AI and labeled data
Agentic AI systems are autonomous, goal-driven agents that perceive environments and take multi-step actions. These systems typically rely on machine learning models (for vision, language, planning, etc.) to process inputs and decide actions.
Data quality plays a pivotal role in developing reliable agents. Inconsistent or outdated information can degrade agent performance and cause erratic behavior. Ensuring data quality means maintaining accuracy, diversity, consistency, validity, timeliness, and relevance. For example, an agent trained on incomplete and erroneous flight data might tell one customer, “All flight tickets are booked”, while telling another, “Two seats are available”, creating confusion. Incomplete, outdated, or inconsistent data drives agents to make dangerous assumptions or produce factually incorrect results.
Conversely, rigorous data cleaning and governance form the first line of defense against such failures. Labeled examples are used to train agentic AI to interpret sensory input and language.
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Labeled data in supervised learning for agentic AI
The autonomous capabilities of AI agents are built upon specialized, task-specific models developed through supervised learning, where AI is trained on large labeled datasets. For visual perception tasks, convolutional neural networks (CNNs) and other deep learning architectures learn from vast collections of annotated images. In natural language processing, models are trained on labeled text corpora, such as transcripts tagged with intents, sentiments, or entities, and mapped to correct responses. This training enables agents to interpret inputs and generate contextually relevant responses.
Computer vision: Labeled images train models for image classification, object detection, and segmentation. These perception modules are vital for robotics and other agentic AI systems.Language understanding: Agents learn intent detection, named-entity recognition, and speech recognition from labeled text and audio datasets. For instance, a virtual assistant learns to interpret commands like “turn on the light” by training on examples of spoken or written instructions paired with corresponding actions.Imitation learning: AI agents learn policies by mimicking demonstrations where expert-labeled actions or recorded behavior act as supervised training data.
In a nutshell, supervised models serve as the “eyes and ears” of the agent. Without labeled data, agentic systems would struggle to develop the perception and understanding needed to perform meaningful tasks.
Labeled data for fine-tuning and adaptation
Large agentic AI models are often trained in stages, with labeled data playing different roles at each step. While initial pre-training may rely on vast amounts of unlabeled data, fine-tuning typically introduces labeled data to adapt a pre-trained model to a specific task or domain. Many agentic systems build on a foundation model and fine-tune it with labeled examples that reflect the specific tasks it is expected to perform. For example, an AI assistant might be fine-tuned on question–answer pairs or dialogue transcripts.
Agentic systems are dynamic and must continuously learn and grow from their experiences. When encountering new scenarios or the input distribution shifts, additional labeled data from the new environment may be used for fine-tuning or retraining. Techniques such as active learning allow the agent to request labels for uncertain cases, while continuous human feedback helps refine performance.
For example, engineers label edge cases (such as unforeseen traffic situations) collected by autonomous vehicles to refine their perception models. Labeled data is essential not only for supervised fine-tuning but also for adapting agents as they evolve in real-world environments.
Alternative learning paradigms
Unlike supervised learning, which needs large labeled datasets to train for each new task, advanced AI can adapt with far fewer labels. Few-shot and zero-shot learning approaches minimize the need for new labeling at task time. A few-shot learning model requires only a small set of examples to adapt to a new task, while a zero-shot learning model relies only on natural language instructions and can still perform the task without any labeled examples.
For example, a language agent might answer a new type of question by being given just a couple of examples in the prompt. In this way, few-shot methods greatly reduce the need for extensive new labeling for each task, though they still rely on the vast “self-supervised” pre-training already embedded in the model.
From AI assistants and chatbots to self-driving cars and collaborative multi-agent systems, labeled data isn’t the only thing models learn from, but it plays a key role alongside other signals. For example, autonomous agents may use LLMs to interpret market news, but are fine-tuned on labeled historical market data. Similarly, software agents rely on standard labels and schemas to interpret the digital world consistently. In short, real-world agentic AI systems almost always include components trained on human-provided labels – from perception to language understanding to task execution.
Conclusion
Agentic AI represents a shift from reactive tools to autonomous, goal-driven systems capable of perceiving, reasoning, planning, and acting with minimal human oversight. Across industries—from healthcare and finance to logistics and customer service—these systems rely on multiple AI agents working together, coordinated through orchestration, to tackle complex problems efficiently. At the core of their reliability and effectiveness is high-quality labeled data: it enables agents to perceive environments accurately, understand language, learn from past examples, and fine-tune their actions over time. Whether through supervised learning, imitation learning, or targeted fine-tuning, labeled data ensures agentic AI can interpret inputs, make informed decisions, and act purposefully. In short, the combination of autonomous agentic behavior and human-curated labeled data drives intelligent, adaptable, and scalable AI systems that transform real-world operations.
