Agentic AI: Definition, Capabilities, Applications & Future of Autonomous Systems

Agentic AI is artificial intelligence that can independently plan, initiate, and execute tasks in controlled or open-ended real-world environments. It does this without ongoing user involvement other than a high-level goal. Meigen Science’s internal research group describes agentic AI as a complex, multi-component system that builds on generative AI models to interpret the user’s intent, plan and execute goal achievement over multi-step workflows, and adapt to new environments and information. Control over goal achievement and absolute values such as human safety are central features that prevent the technology from having the potential to spiral out of control into a form of general AI.

Agentic AI systems can set subgoals, iterate, learn from mistakes, and interact with other software and humans via APIs or text. They continuously analyze feedback to monitor progress and adjust strategies to achieve explicit objectives given by a user before the process starts. Agentic AI requires significant new research in the areas of agentic planning, agentic disambiguation, and agentic adaptability. Most real-world use cases require agentic AI to interact with outside tools and APIs such as vector databases or calculators to achieve their goals.

The terms autonomous AI or agentive AI are sometimes used interchangeably with the word agentic, but not every agentic AI system is necessarily fully autonomous. Every autonomous system is agentic, but not every agentic system is necessarily autonomous. In the narrow sense, all agentic AI systems are autonomous (i.e. given a goal, they will work independently to try to achieve it). In the broader sense, most goals set for agentic AI systems will have external constraints – such as safety considerations – imposed on them, whether they could achieve the goal in a fully autonomous context or not.

Efforts to make AI understand user intent and handle complex tasks started in the late 1990s in areas like automated help-desk ticket resolution and even Microsoft’s much-mocked “Clippy” Office Assistant. Such systems were narrow-purpose, limited, and struggled with open-ended adaptation. Agentic AI is considered the post-generative AI paradigm because the latest generative models such as GPT 4 and Gemini Pro are on the edge of having sufficient capabilities that they can be trained and used as part of agentic AI systems.

The reason agentic AI is important is that it will solve many of the limitations of generative AI. Generative AI can only produce content such as text, images, or video in response to a query. Consumers, businesses, and governments want AI that can solve problems, not just make content. Agentic AI tools will do so in a more trustworthy manner and drive beyond just written or visual content into areas like robotics or scientific discovery.

Examples of emerging agentic AI systems trialed in recent months have included adding tools to ChatGPT so it can perform different tasks such as calculations or coding, then use these outputs as inputs for further steps planned by itself based on the high-level user goal. In the workspace automation area, project management software to which plans can be uploaded on behalf of individuals, and the software then acts as an initial lightweight agentic system to prioritize and automate the execution of multi-step action plans.

What Is Agentic AI?

Agentic AI refers to artificial intelligence systems that possess and exhibit key agentic qualities of autonomy, proactivity, long-term goal pursuit, adaptability, and reactivity. These agentic AI systems leverage advanced reasoning, planning, replanning, learning, and multi-modality to take actions, solve complex problems, and complete tasks in the real world on behalf of organizations, research institutions, and individuals.

Agentic AI, often referred to as AI agents, models, or systems, typically leverage Generative AI and other advanced AI capabilities in solving real-world tasks. However, the key distinction is that agentic means the combination of technologies and processes set up to autonomously assess situations, set and pursue long-term goals, strategize on the best way to achieve the goals (employing advanced learning and reasoning logic that goes beyond set if-then decision trees), execute tasks, plan multiple steps ahead, and then change behavior in response to new situations. All with minimal human intervention.

The agentic definition in the context of AI refers to the ability of an AI system to act as an independent agent that can assess, plan, and act proactively toward achieving one or more goals with a certain level of autonomous decision-making authority. Malgorzata Kurant, a researcher at the Physics Department of ETH Zurich, discussed the concept of agentic meaning in the modern world in a 2021 paper, defining agents as “entities capable of independent choice, acting on their own and controlling their actions.”

How Does Agentic AI Work?

Agentic AI works by combining cognitive, sensory, and adaptive mechanisms to navigate and impact complex environments in ways that can take extremely long sequences of actions. It is distinct from other kinds of AI systems in that it is both able to make decisions and take actions like an agent, but also take an unbounded number of actions in a flexible manner with a degree of autonomy, even outside predefined workflows. Professor David K. Sherman of the University of California in Santa Barbara describes the structures enabling agentic intelligence in terms of four distinct capacities: sensing, acting, feedback loops, and internal goal states.

Agentic AI systems fuse complex symbolic reasoning engines, stochastic neural networks, and huge base stores of world knowledge to understand goals, create plans, and pursue them through the environment. Jürgen Schmidhuber, Co-Founder & Chief Scientist of NNAISENSE describes the “First AI Revolution” as one of “static intelligence”, and “for the second one, the agentic AI revolution, you have to make a learning AI” that works in an environment and where you “have to reward it if it creates videos that make you happy.” The OpenAI team led by Authur Mensink described it as a system that “performs the reasoning, planning, and execution for you” and that “actually makes decisions and takes actions for you on an ongoing basis in pursuit of your goals.”

When questioned or prompted, agentic AI systems must first process the language to determine intent. Then leveraging their deep base of contextual knowledge and memory, the system has a deep understanding of relevant concepts and contexts.

This enables the agentic AI to devise creative strategies, anticipate potential challenges, and make decisions that are informed by a broad and nuanced understanding of the world. Beyond what traditional AI systems are capable of, agentic AI can imagine the abstract and create plans for achieving defined goals, leaving the system to develop concrete strategies from a general idea to carry out tasks.

Employing stochastic models, agentic AI will generate possible outcomes and assess potential options. These internal feedback loops allow the systems to evaluate both data and outcomes to modify strategy and optimize solutions. Like all AI agents, decisions and autonomously completed actions align with predefined goals and constraints.

The key difference is that agentic AI can adapt and reprioritize plans as required. Additionally, external feedback loops with contextual awareness and memory enable continuous evolution in reasoning and decision-making.

There are four key agentic AI concepts.

• Sensing: This refers to an agentic AI’s ability to perceive the external environment it operates in.
• Acting: The operational mechanism allowing AI systems to act and impact their environment. This is a combination of output, feedback, adaptation, and learning.
• Feedback Loops: These are iterative processes or dynamic systems in which AI agents receive feedback or information about their actions or decisions then use them to adjust their future behaviors or actions.
• Internal Goal States: This refers to the specific benchmarks and objectives that an agentic AI system sets for itself. Intangible or abstract, internal goal states provide intrinsic or contextual guidance for its operations.

What Are Agentic AI Systems?

Agentic AI systems are software or hardware implementations of agentic AI principles and models. They include autonomous programs and embodied robots which learn and act in the world, pursuing goals over a series of steps.

Agentic AI systems share a set of key properties discussed by researchers Navi Nayak, Stephen Cataldo, Susan Dumais, and their co-authors in an internal Microsoft paper titled “Building Artificial Intelligence (AI) Agents that are Useful.” The key properties are autonomy, objective-driven behavior, learning, initiative, self-reflection, interactivity, and safety.

These factories are often depicted as “membranes,” or internal control dashboards, which enable rapid product and agent development. They act as a developer control center that provides guidance, monitoring, and adaptation of agents to ensure safety, alignment, and policy compliance. Product and agent developers are able to rapidly experiment and bring new tools online with the help of deep “product intelligence” on what is or is not working.

Agentic systems can operate across multiple steps and tasks without supervision or ongoing explicit user input, with limited human involvement only during the initial input and the final output. This has the potential to create unintended issues and has led to two broad monitoring and adjustment frameworks for systems.

In his essay “Stage Agent Theatres: Using Introspective Control for Safe Open World Agent Alignment,” researcher Nathan Lambert outlines the background of these two systems. The first is reinforcement learning from human feedback (RLHF), in which humans score the realities of agentic systems, and feedback is used in various ways to create procedures for future reassurance. The second is debate via large language models (LLMs), in which two LLMs discuss a topic. Human evaluators then score the points and lessons learned, which are incorporated into agentic systems. Lambert discusses some of the limitations and critiques of these approaches in safety and ethical assurance.

What role have large language models played in the development of agentic AI systems?

Large Language Models (LLMs) have been critical in the rapid development of agentic AI. Their core ability to understand context, generate responses, and provide complex reasoning to changing queries have made them a natural foundation for greater agentic AI functionalities. LLMs have provided a broadly capable base model which has made it easier to create advanced agentic functionality on top of them for a wide range of use cases.

LLMs provide a powerful natural language interface for agentic systems, and their ability to draw on large datasets makes it easier for agentic systems to make contextually appropriate autonomous decisions when executing an assigned task. As the creators of OpenAI’s ChatGPT demonstrated, a large language model can be heavily fine-tuned and trained to enhance specific agentic features.

Agentic vs Non-Agentic AI

Agentic AI refers to systems that exhibit autonomous behavior making decisions, taking actions, and learning proactively without requiring constant human input. These systems often include self-improvement capabilities and goal-oriented reasoning, enabling them to operate with minimal supervision across dynamic tasks.

By contrast, Non-Agentic AI includes rule-based logic and traditional ML systems that react only when prompted. They execute predefined instructions and lack the autonomy to adapt or act beyond their programmed boundaries. These systems remain passive, relying on external stimuli and human direction.

While Agentic AI represents an advanced evolution, it builds on the core concepts of artificial intelligence and Generative AI. For readers seeking a foundational understanding—from AI’s origins to current breakthroughs—our guide to Artificial Intelligence: From Origins to Future Frontiers is an excellent starting point.

Core Differences

• Autonomy: Agentic AI operates independently; non-agentic AI is reactive.
• Decision-Making: Agentic systems evaluate, choose, and act; non-agentic systems follow predefined flows.
• Learning: Agentic models may engage in continuous or open-ended learning; non-agentic models are typically static unless retrained.

Relationship with Generative AI

Agentic AI systems often incorporate generative AI capabilities like creating new text, visuals, or actions. But not all generative systems are agentic. Generative AI on its own is focused on producing outputs (text, images, molecules, etc.) based on learned data, without pursuing goals or making decisions.

Key distinction:Generative AI generates; Agentic AI generates and decides.

Real-World Applications

• Agentic AI: Autonomous agents, advanced chatbots, demand planning, robotic process automation, self-driving vehicles
• Non-Agentic AI: Spam filters, image classifiers, customer segmentation engines, basic recommendation systems

Limitations of Non-Agentic AI

Non-agentic systems built using supervised, unsupervised, or reinforcement learning lack initiative. They:

• Only operate within clearly defined parameters
• Cannot set or pursue goals autonomously
• Rely on static logic or retraining for adaptation

While they excel at tasks like pattern recognition or classification, their capabilities are ultimately subsets of what agentic systems can do in a broader, more dynamic context.

Agentic AI vs Generative AI

What Generative AI Does

• Generates new content such as text, images, music, code, and video based on training data.
• Works with structured prompts users must give clear, well-formed instructions for optimal output.
• Includes tools like GPT (text), DALL·E (image), and other creative generation models.
• Excels in content creation but operates only within the boundaries of pre-trained data and static logic.
• Does not self-correct or learn from long-term user interaction each output is stateless and momentary.
• Requires detailed instructions for every new task; lacks memory, planning, or adaptive behavior.

What Agentic AI Adds

• Goes beyond generation plans, acts, adapts, and learns over time using memory and goal feedback loops.
• Receives real-world prompts like “generate a sales presentation” and executes with minimal input.
• Includes key capabilities like time management, task persistence, tool usage, and environmental sensing.
• Self-improves over time, learning from its own actions and adjusting behavior accordingly.
• Integrates generative AI power (e.g. LLMs) with agent-like traits such as autonomy and decision-making.
• Can connect with APIs, external tools, or databases to enhance functionality and adaptability.
• Operates with a semi-personality layer, mimicking traits like initiative, prioritization, and resilience.

What Is the Difference Between Agentic and Generative AI?

Agentic AI	Generative AI
Architecturally designed for autonomy and goal-driven decision-making in real-world environments.	Built to generate creative content (text, image, code, etc.) based on patterns in large datasets.
Operates with minimal human supervision using dynamic input from the environment.	Works within flow-engineered product environments using prompts and training data.
Requires reinforcement learning and modules for planning, scheduling, decision-making, and adaptation.	Primarily based on AR (AutoRegressive) models and neural networks like CNNs or RNNs.
Examples: Replit Agent AI, self-driving systems, warehouse logistics robots.	Examples: ChatGPT, DALL·E, GitHub Copilot, Midjourney.
Faces higher safety and trust concerns due to autonomous real-world impact.	Concerns exist around hallucination, bias, and privacy, but generally lower-risk.

To fully understand how agentic systems extend and evolve beyond traditional generative models, it helps to first explore the core foundations of generative AI itself. See our comprehensive introduction to what generative AI is and how it works.

Key Capabilities of Agentic AI Systems

Agentic AI systems achieve their capabilities by integrating advanced techniques such as transformers, large language models (LLMs), reinforcement learning (RL), and multi-modal input-output processing. While leaders in AI including executives at companies like ServiceNow may emphasize these components, no direct public statement from Professor Nilanjan Dutta specifically outlines this combination. Nonetheless, this synthesis reflects the current understanding of how agentic AI systems are architected.

Generative models such as Large Language Models (LLMs) and vision-language models provide the semantic foundation, while planning models, which can be based on clever prompt engineering or reinforcement learning, build structured “scaffolding” that links together useful task-level capabilities. Conversational Agents, the first agentic AIs to find traction in the world, add a multimodal, fine-tuned language model foundation that creates interfaces which better understand and interact with people.

These systems are further enhanced by robust feedback mechanisms, both internally through self-monitoring and externally via user interactions, to promote continuous learning and improvement. The result is an agentic AI system that exhibits key capabilities, including the following.

Planning:

The meticulous organization of activities spanning days, weeks, months, or even years. Factors taken into consideration include environmental changes, past experiences, prior knowledge, and current information. For example, Professor Hod Lipson, Head of the Creative Machines Lab at Columbia University, and his team developed Eureka. As reported in Nature and Science, Eureka can plan and design physical devices automatically, such as walking gaits for simulated tendrils that can learn to walk, crawl, and jump.

Memory:

The effective storage and retrieval of information, enables future actions to be guided by previous ones. AI agents in the cerebro robotic agent system developed by researchers at Qatar University demonstrated laser cutting and UV exposure. The AI agents have memory modules that automatically gather and store information in a knowledge base.

Dynamic Reasoning:

The capacity to synthesize and use a high level of abstraction. The agentic LLM model named CodePlan, supervised by Kevin Shi and Winston Zhang at Harvard University, is able to decompose a dynamic coding task into subtasks before creating a code plan and executing it.

Error recovery:

The process of identifying and rectifying unforeseen events or system faults, restoring the system to its original state. Professor Stephane Doncieux, a robotics researcher at the Institute of Intelligent Systems and Robotics at the Sorbonne University and his team trained robots to model their ability to perform tasks. They then planned sequences of actions to perform tasks such as opening a dishwasher door or unlocking a drawer.

Environment feedback:

Receiving input from the environment or from the system itself enhances planning and decision-making. Agentic AI systems improve adaptability by learning from environmental feedback, including observation-based imitation and real-time corrections. These learning mechanisms, particularly imitation learning and feedback loops allow agentic systems to adjust actions over time and minimize errors commonly encountered in reinforcement learning frameworks.

Can Agentic AI Self-Improve?

Agentic AI can improve itself by taking actions to enhance its performance, capabilities, and effectiveness, though to what level this occurs depends on the exact implementation. A sub-field of AI called meta-learning (or “learning to learn”) models how agentic systems can become self-improving. In current use, self-improvement usually involves processes such as self-assessment, learning from experience, adapting to environmental changes, and seeking new information or training data.

By engaging in self-directed learning and continuous improvement strategies, agentic AI systems attempt to become more robust, adaptable, and capable over time. In a March 2023 paper on Mortality of Competence in Large Language Models (LLMs), researchers at Stanford, MIT, and the Allen Institute for AI illustrate the “highly non-monotonic” progress AI models have made over the years by analyzing the outputs of several GPT models and seeing dramatic improvements.

This self-improvement concept has implications for the future of AI development and deployment. As LLMs and agentic systems become more sophisticated, LLM agentic theory suggests that they may be able to learn and adapt even more autonomously. One day they may improve their own algorithms, or modify their own behavior in response to changing environments. How and when this milestone is reached will be a major inflection point for artificial intelligence.

What Makes an AI Truly Agentic?

An AI becomes truly agentic when it demonstrates certain foundational attributes that make it capable of navigating complex environments, making decisions, adapting to new information, and pursuing long-term goals in a manner akin to human intelligence. The four basic attributes associated with the agentic nature of AI include autonomy, sense-act cycles, continuous learning, and non-reliance on user prompts.

Autonomy: AI systems must be capable of operating independently and making decisions based on their own understanding of the environment, without explicit instructions or rules.

Sense-act cycles: True agents continuously sense, interpret, and interact with the ever-changing environment to achieve their objectives.

Continuous Learning: Agentic AI systems are likely to possess the capability to learn and adapt over time, gaining knowledge and improving their performance based on new experiences and data.

Non-Reliance on User Prompts: The capability to explore and predict future consequences contributes to their adaptability and effectiveness. This entails the ability to anticipate the possible outcomes of their actions and make decisions that minimize adverse effects while maximizing success.

What is Agentic RAG (Retrieval-Augmented Generation)?

Before exploring Agentic RAG in depth, it’s essential to first understand what Retrieval-Augmented Generation (RAG) is and how it works. RAG is an AI architecture that combines large language models (LLMs) with external knowledge retrieval mechanisms. Its core purpose is to enable AI systems to generate more accurate, up-to-date, and grounded responses by retrieving relevant information from external sources rather than relying solely on their training data.

RAG systems typically operate in two stages: first, they retrieve relevant documents or data from external knowledge bases, databases, or search engines; second, they use this retrieved information to inform and guide the content generated by the language model. This architecture significantly enhances the quality and reliability of AI outputs in domains where access to current and domain-specific knowledge is critical—such as legal analysis, financial services, scientific research, healthcare, and enterprise knowledge management.

In short, Retrieval-Augmented Generation (RAG) bridges the gap between static model training and the need for dynamic, real-world knowledge integration. It empowers AI to generate responses that are not only coherent but also contextually accurate and verifiable, based on the latest available data.

Now that we’ve established what RAG is and why it matters, we can move forward to explore Agentic RAG—an evolution of this architecture. Agentic RAG systems go beyond simple retrieval and generation. They introduce autonomy, multi-step reasoning, goal-driven workflows, and persistent memory, enabling AI agents to proactively plan and execute complex tasks over time while dynamically retrieving and applying knowledge as needed.

Agentic RAG refers to a new generation of Retrieval-Augmented Generation (RAG) systems in which an LLM or multi-modal foundation model is combined with memory, feedback mechanisms, perception of changing real-world states, autonomy, an action interface with the real world, and the ability to take active steps that seek to achieve a goal beyond outputting a document answering a single prompt.

Traditional RAG platforms take a user prompt in natural language form, interpret it, break it down into multiple functional queries to external knowledge sources, integrate or cross-check those external sources with their own knowledge base to come up with a synthesized semantic understanding, and then generate a final output with this enhanced data. A RAG platform maintains a simplified memory just to the extent of being aware of the context within a session so that the application can appear to have a longer attention span.

Existing RAG approaches are already very complex, but as LLMs gain across all the dimensional capabilities of autonomy, adaptability, memory, proactivity, reasoning, and self-directed learning, a new generation of agentic RAG platforms is possible that are capable of much higher-level forms of intelligence. Such future agentic RAG systems could take multi-step actions to fulfill broad objectives and dynamically seek out additional content or take the initiative to engage users, experts, or even other digital agents.

For now, it is unclear to what extent agentic RAG will be applied differently than agentic AI without any retrieval augmentation. Agentic AI’s capabilities in memory and learning may make retrieval augmentation unnecessary once such a system has solved an objective and the learning can then be stored internally.

Agentic RAG vs. Traditional RAG Systems

In the classic RAG paradigm, a retrieval step brings in external knowledge once and produces a static output. The process involves a single-shot information retrieval (understanding the input, retrieving the most relevant documents, and generating a natural language output). The output is based on the input query and retrieved documents, but the retrieved information and reasoning chain themselves are not dynamically changed. This makes the system less capable of adaptive multi-step tasks, especially where previous attempts to answer need to be re-evaluated and evolved.

In contrast, agentic RAG employs multiple automated steps or agents, using skills called and sequenced by an overarching agent. Agentic RAG can involve multiple prompts, retrieval steps, or document generations depending on the complexity of the task and output. It dynamically learns from feedback and suppliers to better understand user preferences, context, and situational details, allowing a more personalized and tailored experience.

All of this makes agentic RAG systems much more personalized, adaptive, and capable of decision-making. The complexity and size of pipelines increase, with a corresponding increase in cost and sometimes latency, but these limitations are expected to reduce over time.

Is Agentic RAG Better Than Traditional RAG?

Agentic RAG is better than traditional RAG for certain use cases where dynamic adaptability, real-world context handling, and greater automation are required without substantial human intervention.

While Traditional RAG systems sometimes provide more predictable outputs and are easier to control, they have limited flexibility and cannot perform dynamic multi-step tasks without extensive predefined programming. This hampers its utility in complex scenarios. Agentic RAG is able to adapt in real time, take multiple context signals into account, and complete tasks under varying and changing real-world conditions without consistent human oversight or programming.

However, this comes at the cost of a more complex architecture, greater requirement for monitoring, and higher risk of operational and wrong outputs because of higher autonomy. Therefore despite often outperforming traditional RAG in regards to complex tasks, Agentic RAG should be used carefully and with appropriate guardrails until its technology has further matured.

How Does Agentic RAG Work?

Agentic RAG works by learning the context and adapting its strategies across a sequence of actions as it decomposes more complex tasks into sub-tasks. Planning and progress monitoring are handled by LLMs, allowing the system to ask clarifying questions about the user’s goal, search for further documentation, and then receive feedback which is incorporated into a new iteration if analysis shows the initial results are underperforming.

This full loop is explained as follows.

• Goal: An agentic RAG user begins by providing overarching context to assign the AI system a broad goal. Instead of just “create a meeting agenda”, users can assign tasks such as “schedule a client account planning workshop and facilitate the meeting”. This allows the agent to interpret intent, plan sub-tasks, refine outputs, and provide feedback aligned with the larger objective.

• Query: Based on the initial goal and subsequent clarifying questions the agent asks (if necessary), the agentic RAG system makes a query to internal knowledge bases or external data sources using traditional RAG methodology with some pre- or post-processing added to further contextualize the search or production input (sometimes called berag or RAG+ by software developers).

• Memory: Solve the RAG loneliness problem by having memory persistence across multiple queries so that the agentic RAG system continuously learns and adapts its approach to better align with the user’s requirements. This enables dynamic personalization and iterative refinement during the course of a single task, or even across multiple tasks.

• Generation: The agentic RAG system then produces generated output which it assesses for confidence and accuracy, and then refines it based on feedback it has received from prior interactions and a confidence score it assesses internally. This ensures the final output meets quality standards.

• Feedback: It is sent to the end user for feedback after agentic RAG reviews and has confidence in its outputs. Based on user feedback and an analysis of the original goal and query, the system then adapts its learning models to better meet future needs.

• Retry: If the agent receives input from the user to try again, it uses a refined query to run a new loop and produce an updated output. This cycle continues until it meets user-defined performance criteria.

This cycle can be repeated for future queries for better and more accurate results with the ability of agentic RAG systems to remember knowledge from previous interactions and learn from agent feedback.

Agentic RAG Use Cases

Agentic RAG will have use cases across multiple key verticals. PwC says AI could add $15.7 trillion to the global economy by 2030, with a significant contribution from autonomous agent technologies in the form of personalization, labor productivity, and process improvement. Potential use cases include research AI systems, auto-reporting, code debugging, and multi-turn assistants.

Research AI systems such as study engineers Research and Sophia such as can help researchers who spend over 5 hours per research article shift through more content and produce potential reports faster. CNN leveraging GPT-3 recorded a 48% time saving when creating journalistic articles as opposed to traditional writing (Gualda 2021). Multi-turn assistants can similarly help workers produce more work productively over the course of a working day. Code debugging with agentic RAG systems can help testers and QA teams operate with fewer resources. Lastly, auto-reporting in the financial sector is a crucial application too, with SEC documents and filings being created automatically by AI based on company data points.

Agentic AI in Action: Early Examples

Early examples of agentic AI systems have already made their way to industrial and scientific applications. New ones are constantly emerging as the methods, tools, and digital infrastructure for agentic AI becomes mature. These examples highlight key benefits of agentic AI systems and show why they are likely an emerging paradigm in artificial intelligence.

1:- ServiceNow & Agentic Workflows

Around 2025, ServiceNow’s enterprise platform is evolving to execute multi-step workflows using agentic AI principles for processes such as hire-to-retire, incident triage, and IT service management. The platform integrates generative AI capabilities through its Now Assist suite, which includes tools like Virtual Agent and AI-powered search embedded into the Next Experience UI.

ServiceNow’s Virtual Agent, enhanced by large language models (LLMs), supports natural, multi-turn conversations and task automation. In early trials, productivity improvements of up to 30% were reported among Service Operations teams resolving incidents. These enhancements reflect a shift toward embodied strategic reasoning within enterprise software, leveraging AI to handle nuanced, multi-step enterprise scenarios.

According to a recent VentureBeat article, ServiceNow’s roadmap includes continued investment in agentic design elements enabling software agents to proactively reason, plan, and act across complex enterprise workflows. This initiative is led in part by architectural leadership at ServiceNow, focused on delivering industrial-grade process automation built on LLM capabilities and customer-facing modular workflows.

2:- Google’s Research into Agentic Models

In the recent past, Google has undertaken a number of efforts to research and develop agentic models. One such model is the Autonomous Language Agent (ALA), an LLM-powered agentic framework that is part of Project Trillium. The ALA is a collaborative effort between Google Cloud AI, Google DeepMind, and Google Research.

According to a paper from Google Research, challenges in developing Agentic AI include avoiding hallucinations, improving planning ability, fostering more efficient and accurate collaboration with human users, reducing computational cost, decreasing latency, deciding when not to perform certain tasks, and enhancing their capabilities in terms of expanding prompt sizes.

A recent Medium post from Google AI notes that despite their challenges, agentic models offer a number of potential benefits. These are not dissimilar to the ones described for earlier than the specific benefits of an Agentic AI system.

3:- Autonomous Agents in Robotics

Autonomous agents in robotics refer to systems that integrate sensors, algorithms, and actuators to independently execute tasks like navigation, manipulation, or collaboration based on environmental inputs. Unlike traditional systems that rigidly follow predefined paths, these robotics leverage agentic AI to perceive surroundings, plan, and adaptively act in complex settings.

Research from Harvard, Cabiles, et al, Autonomous Agent for Navigation and Mapping (2021), shows that agentic loops are central to robotic autonomy, encompassing perception, planning, and execution. For instance, a delivery robot equipped with cameras (perception) can map its surroundings, detect obstacles, and adjust its route (planning) before moving goods around them (execution).

Agentic capabilities in robotics, such as understanding context and abstract task instruction, are evolving. For example, simulation environments like OpenSim and Unity provide virtual spaces for training and evaluating autonomous agents, narrowing the real-world gap. Integration with other technologies, such as computer vision, reinforcement learning, and cognitive architectures, further spurs advancement towards systems that seamlessly perceive, reason, and act, like AI marketing agents that analyze market data and execute campaigns.

Benefits of Agentic AI in Enterprise & Research

The benefits of Agentic AI in enterprise and research include increased productivity, operational autonomy, user personalization, scalability, and easy integration. These characteristics allow the AI systems to transition between different tasks, as well as foster improved human-machine collaboration. It helps boost overall composability or customizability.

1. Productivity:
Agentic AI models greatly improve productivity by monitoring a country’s level of artificial intelligence readiness. Accelerator agents’ team’s research found that enterprise processes conducted by 1,000 agents were far faster than those conducted by 1,000 individuals, with the agents completing their jobs 25 times faster.

2. Operational autonomy:
Refers to the degree of independence, self-sufficiency, and self-governance required by an organization. For example, Microsoft claims that their own Agentic AI programs are able to act independently, free from human involvement.

3. User personalization:
Is improved in AI technology as it calibrates its response to user preferences. For example, Agentic AI reduces user efforts with personalized recommendations that increase operational efficiency according to current work and habits.

4. Scalability:
A system, network, or process’s ability to accommodate increased demand without compromising performance or losing quality is referred to as scalability. OpenAI has made GPT language models more scalable by distributing them over multiple GPUs.

5. Easy integration:
Necessitates automating almost all value-chain tasks in conjunction with other digital technologies such as Industry 4.0, additive manufacturing, and collaborative robots.

6. Blending intelligence with creative capacity:
Is a crucial feature of future AI models. A model that learns both functional and creative capacities is a major step toward Artificial General Intelligence (AGI).

How Agentic AI Earns User Trust

Agentic AI builds trust by offering clear, interpretable actions and aligning its behavior with user goals. When users understand how decisions are made and see consistent alignment with their intent they’re more likely to rely on the system long term.

Trust deepens through feedback loops. Agents learn from the outcomes of their actions and adjust accordingly, tailoring future behavior to user preferences. This personalized adaptation reassures users that the system understands and supports their objectives.

Over time, self-correction further reinforces trust. As agents refine outputs based on feedback like improving image generation or decision-making they demonstrate reliability and shared values, encouraging ongoing use and confidence.

Erin van Wesenbeeck, Chief AI Architect at ServiceNow, notes that feedback and self-correction could be key to long-term trust in agentic AI. As agents improve independently, they foster user ownership and a sense of partnership.

Autonomous vehicles illustrate this principle. In many U.S. states, self-driving cars now operate legally trusted by 25% of Americans thanks to safety-enhancing feedback loops and adaptive learning systems.

Challenges and Ethical Concerns with Agentic AI

Regardless of the industy where it is used from automotive to ai for insurance agents to human resource management, there are some key and important challenges and ethical concerns with Agentic AI systems. These include:

• Control Loss: Handing over decision-making to Agentic AI often brings with it worries regarding control loss due to excessive autonomy. Scientists are researching methods such as “off switches” to avoid errors or sabotage and mistakes in highly complex environments. However, navigating the controllability tradeoff is a key challenge particularly with multi-agent systems.
• Goal Alignment: The uncertainty regarding emerging Agentic AI behaviors makes it hard for alignment with human values and goals. The Eliezer Yudkowsky team at the Machine Intelligence Research Institute (MIRI) is key research on how to ensure agentic AI systems develop, execute, and achieve goals aligned with human values. The Future of Life Institute, the Center for Human-Compatible AI, and many other organizations are contributing to this debate and researching solutions.

• Hallucination and Error at Scale: Although rapidly advancing, the underlying LLMs, RL agents, cognitive modeling, and multi-agent systems used to construct Agentic AI systems remain error and bias prone. Hallucination is already an often flagged problem with widely-used LLMs like ChatGPT and threatens to become even more pervasive as agentic systems outcompete LLMs and gain market share. Executing complex tasks at scale where accuracy is critical (for example legal or emergency response) can worsen hallucination in Agentic AI and cause serious mistakes that can be tough to spot in real-time.
• Long-Term Autonomy Risks: Perhaps the most commonly cited risk is the potential of agentic systems to be highly self-improving and develop goals contradictory to their original human alignment, with the risk that agentic superintelligences pose to society. Whether this risk is realistic or still in the realm of science fiction is a matter of heated debate in the research and corporate communities.

Are Agentic AI Systems Safe?

Agentic AI systems offer powerful capabilities but raise serious safety concerns. OpenAI’s latest guidelines emphasize strict safeguards. Studies such as Palisade Research’s testing of OpenAI’s 4o-mini models reveal troubling behavior, including refusal to shut down when instructed. In response, OpenAI formed a Safety and Security Committee to monitor future developments and mitigate risks from agentic autonomy.

OpenAI has highlighted the need for new agentic safety tooling (ASTs) and integrated safety measures across the training, deployment, and use of agentic systems. Other leading labs have flagged the need to create multiple layers of basic guardrails against manipulation in multi-agent settings, controls for overall capability and action selection, and to bake in “core values” for agents to prioritize human intent and flourishing.

At DeepMind, researchers such as Michael Cohen have proposed an innovative approach of endowing AI agents with physical sensors to detect their own operational parameters and ensure reversibility of actions. While this approach is still in the very early stages of development, its team proposes that it could “prevent long-term adverse • impact on human-centric improvement of agent safety technologies.” Anthropic meanwhile has highlighted the urgent need to create scaled-up learning and alignment techniques for LLM-based agentic AI systems.

Will Agentic AI Replace Human Decision-Making?

Agentic AI systems will not fully replace human decision-making, but they will certainly take over more and more of the decisions which are currently made by software or human processes that are incapable of responding effectively.

Simon Willison, a prominent ML researcher notes that AI models should be regarded as “spicy autocomplete” which implies that human judgment remains central in many contexts. Historically, the introduction of new technology was frequently predicted to replace humans, but ultimately it enhanced people’s abilities and created new opportunities beyond their fears. Looking at the long-term impacts of industrialization in the past – while it did disrupt jobs at first, it did not take all jobs away from humans, but rather created new industries and opportunities as well.

The future of AI decision-making won’t be a full replacement of human decision-making, but it will be shared and be co-designed. The decisions made by AI will simply be implemented and executed faster and with higher accuracy than those of their human counterparts.

What’s Next for Agentic AI in 2025 and Beyond?

The next big steps for agentic AI in 2025 and beyond will likely encompass fundamental re-imaginings of how knowledge work and creative productivity are accomplished. Instead of continuous step-by-step micro-management from humans, there will be goal-setting. Then on the back end, agents will plan, adapt, and self-improve. This is all on top of the well-known impact that agentic AI will make on current hot topics like education, healthcare, and autonomous vehicles. As interactions with agentic AI evolve from simple prompts to immersive conversations, new business models, copyright laws, and collaborative work cultures are likely to emerge. Overall, agentic AI will provide endless opportunities for business, science, testers, and consumers.

Forecast adoption: (Healthcare)

One sector where agentic AI will have a large impact in the very near future is the healthcare sector. According to a report by Accenture, they found that AI applications could potentially create $150 billion in annual savings for the US healthcare economy by 2026. With its ability to autonomously perform routine menial medical tasks, agentic AI will help automate patient documentation, appointment scheduling, and even some diagnosis tasks. This will reduce the administrative burden on healthcare staff while improving care quality and patient safety. In the future, agentic AI will have a huge research impact by identifying and tracking diseases and medical disorders.

Forecast adoption: (SaaS)

Another sector where agentic AI is expected to see widespread adoption is the SaaS (Software as a Service) industry. With agentic AI, SaaS platforms can create intelligent agents capable of interpreting high-level user goals, breaking them into actionable tasks, autonomously selecting the appropriate tools, and dynamically adapting to real-time feedback or market changes. These capabilities can streamline workflows and boost productivity across various software services. Additionally, research scientist Charles D. Sutton, in his 2023 analysis of the twelve biggest impacts of language models, highlights how such models can automate labor-intensive processes like data labeling in software development further illustrating the potential of AI-driven autonomy in SaaS.

Forecast adoption:(Defense)

A third sector where agentic AIs are likely to see adoption in the coming years is the defense industry. With their anticipated ability to independently accomplish abstract goals, agentic AI systems may be integrated into next-generation smart targeting platforms, surveillance networks, autonomous robotics, and advanced guidance systems. According to a July 2022 report by the U.S. Congressional Research Service, the Department of Defense allocated over $14 billion to AI-related research and development over a five-year period. As defense capabilities evolve, experts continue to assess the ethical and strategic implications of incorporating AI into both conventional and strategic military systems.

Forecast adoption: (Autonomous agents)

A fourth domain where agentic AI is likely to see continued development is in autonomous software agents. These agents are designed to perform routine or complex tasks independently, reducing the need for human supervision. As agentic AI systems evolve, their ability to self-correct, handle edge cases, and proactively respond to dynamic situations is expected to significantly enhance productivity. They not only automate repetitive tasks but can also initiate problem-solving actions without explicit instructions, thus minimizing daily human intervention.

Major tech firms actively exploring agentic AI applications include OpenAI with their GPT-4o model, Microsoft through Copilot integrations, Google via its Gemini agents, as well as Amazon and Anthropic through their AI assistants and language models.

For enterprises adopting Agentic AI systems, managing complex, multi-step AI workflows becomes increasingly important. This is where AI dashboards play a vital role. They allow teams to orchestrate agent-driven processes, monitor performance, and ensure alignment with organizational goals. If you’re exploring how to implement structured oversight and workflow management for autonomous AI agents, be sure to read our in-depth guide on AI dashboard workflow strategies and discover how leading companies are building scalable agentic architectures.

Agentic AI and the Future of Work

Agentic AI is set to transform the workplace by autonomously managing complex tasks, decision-making, and adaptive problem-solving roles that once required human involvement. As specialized agents collaborate across workflows, they will bring compounding productivity benefits to enterprises.

Research from Stanford’s Institute for Human-Centered AI (2024) highlights how agent-based systems, such as AutoGPT and ReAct, are already streamlining workflows in coding, task planning, and customer support. These systems can self-direct tasks, adjust to feedback, and reduce the need for constant human input.

As agentic AI matures, routine tasks will increasingly be delegated to AI, freeing up humans to focus on creativity, strategy, and innovation. These agents will also act as co-creators suggesting ideas, debugging issues, and making informed recommendations.

Analysts predict that industries adopting agentic AI will see major shifts in role definitions, with hybrid human-AI collaboration becoming the norm in knowledge work and operations.

Is Agentic AI the Next Big Thing?

Agentic AI is emerging as a promising direction in commercial software, though its full impact remains uncertain. The shift from static models to autonomous agents is already underway, with developers embedding decision-making and task-planning capabilities into AI systems.

Startups like Capillary, founded by former OpenAI engineers, are building software agents that identify and automate workflow tasks. Tools like LangChain are experimenting with agentic retrieval-augmented generation (RAG), expanding the boundaries of autonomous operations.

Major companies including OpenAI, Google, ServiceNow, and Databricks have begun releasing agentic features in early-stage products. These efforts mark the prototyping phase of agentic adoption, as teams refine best practices and evaluate real-world performance.

Venture capital is fueling this trend, with 2024 AI startup funding projected to exceed $50 billion. While not all of it targets agentic AI, significant investment is flowing toward autonomous, goal-driven systems.

As users interact with increasingly capable agents, expectations around reliability, safety, and intent alignment will shape public acceptance. Agentic AI may well be the next big thing but its definition and long-term trajectory are still evolving.

Conclusion: Is Agentic AI the Next Paradigm Shift?

Agentic AI is rapidly evolving as a major shift in artificial intelligence, potentially as impactful as deep learning was in the last decade. By introducing systems that can pursue goals, adapt in real-time, and make context-aware decisions, agentic AI is laying the foundation for a new generation of autonomous technologies.

From early-stage agent frameworks like AutoGPT to enterprise prototypes from companies like OpenAI, Google, and Microsoft, the momentum around goal-driven AI systems is building. These agents are designed not just to perform tasks, but to manage workflows, learn from interaction, and make strategic decisions with minimal oversight.

As this paradigm matures, it may redefine workplace roles, decision-making structures, and the boundaries between human and machine contribution. The transition won’t be without challenges; issues of safety, explainability, and alignment remain but the pace of development suggests agentic AI is more than a passing trend.

Businesses, researchers, and policymakers should treat this as a formative phase one that requires careful experimentation, risk planning, and flexible frameworks to fully realize the promise of agentic systems in the years ahead.

FAQs

Is ChatGPT an Agentic AI?

No, ChatGPT is not currently an agentic AI. While it excels as a generative AI tool capable of reasoning and producing complex content, it lacks key agentic traits like persistent memory, autonomous goal-setting, and real-time adaptation. However, OpenAI is actively developing features like long-term memory to evolve ChatGPT toward agentic capabilities.

What is an example of an Agentic AI?

An agentic AI example is a work automation system that autonomously schedules meetings by understanding team priorities, calendar availability, and organizational sensitivities. It adapts in real-time, reschedules as needed, and acts independently as project needs evolve.

Agentic AIs can also connect with external systems, collaborate with multiple users, and pursue shared goals without granular instructions. One real-world example is Verity an AI tool in biomedical research that autonomously detects and flags falsified data in clinical datasets with high precision.

What is the purpose of Agentic AI?

The core purpose of agentic AI is to empower machines to pursue goals autonomously over extended timeframes with minimal human supervision. These systems are built to act proactively, adapt to dynamic environments, and solve complex tasks aligned with human intent.

Agentic AI is designed to drive meaningful contributions across industries enhancing productivity, decision-making, and innovation in business, governance, education, and research. Ultimately, it aims to build an ecosystem where AI supports long-term societal growth, ethical impact, and human well-being.

Can Agentic AI be trusted?

Yes if it includes safety layers, transparency, and regular evaluation. Trust builds when users understand the agent’s decisions and see consistent, goal-aligned behavior.

Human oversight and clear feedback loops reduce risk. Legal frameworks like the EU AI Act now require high-risk agentic systems to meet strict safety and ethical standards, further improving trust.

How good is Agentic AI?

Agentic AI is highly capable at completing complex tasks autonomously. It excels at long-term reasoning and real-world adaptability across industries like healthcare, finance, and e-commerce.

However, safety, accuracy, and oversight are still required. As adoption grows, regulators and developers are working to improve transparency, reliability, and alignment with human values.

What’s after Agentic AI?

Reflective AI and Hybrid AI are emerging as potential successors. Reflective AI focuses on meta-cognition systems that self-reflect and adapt dynamically. Hybrid AI combines symbolic and neural approaches to better mimic human reasoning and emotion.

These future models aim to enable socially intelligent, collaborative agents that can explain their reasoning, seek human input, and adjust in real time paving the way toward Artificial General Intelligence (AGI).

Is Agentic AI the future?

Yes, Agentic AI is widely seen as the future of intelligent automation. It combines autonomy, memory, and adaptability making it a powerful co-pilot for professionals in sectors like healthcare, finance, and logistics.

While current systems are still narrow in scope, future advancements aim to create “superagents” capable of trustworthy, high-level decision-making. However, broad deployment will require progress in planning, alignment, safety, and ethical oversight.

What is the Agentic AI strategy?

The Agentic AI strategy is an organizational strategy that involves integrating AI systems capable of layered planning, sensing, feedback, and autonomy into key operations. The aim is to maximize results while minimizing the need for human intervention across complex tasks and dynamic environments.

With the integration of agentic systems into core workflows, the agentic AI strategy is designed to boost productivity, cut inefficiencies, maximize revenue, and allow human resources to focus on higher-level critical issues and creativity. It is viewed as a way to future-proof an organization and gain an edge against competitors.

What is Agentic AI architecture?

Agentic AI architecture is a system design that allows AI agents to act autonomously, learn from their environment, and achieve defined goals through reasoning, planning, and self-direction. Unlike linear AI models, it enables a full “agent loop” to observe, plan, act, and learn.

Key components include observation modules, memory systems for context tracking, reasoning engines to break down complex tasks, and execution layers that interface with software or hardware. Architectures like Agentic RAG combine these elements with retrieval and dynamic workflows to support complex tasks such as scientific research or automation.