What is Agentic AI and How can Enterprises Use it to Revolutionize Operations in 2025?

Quick Summary

• Agentic AI is the next frontier of AI innovation building on the previous AI models and capabilities.  

• Agentic AI can act without constant human intervention and complete complex, multistep tasks.  

• Agentic AI is built with foundational principles like scalability, modularity, and specialization in mind.  

• While previous AI models automate surface-level repetitive tasks, agentic AI takes action to solve complex, multistep problems that surround modern enterprises.

  

Introduction

Not very long after AI became ubiquitous across every industry, its next frontier is here in the form of Agentic AI. If artificial intelligence and, particularly, Generative AI promised to eliminate the inefficiencies of the past, Agentic AI has the potential to eliminate the inefficiencies of AI models and capabilities we know so far. Agentic AI has the ability to adapt in real-time, act autonomously with minimal human intervention, and solve multi-step problems with a contextual understanding. While traditional AI systems could automate regular tasks limited to rule-based understanding, Agentic AI can handle complex enterprise tasks by devising strategies, learning from problems, and making decisions based on preset objectives. Let’s see how Agentic AI can revolutionize enterprise operations in 2025.  

**Agentic AI Definition**

The Agentic AI definition by Enver Cetin, an AI expert at the global Experience Engineering firm, Ciklum, as mentioned in the Harvard Business Review, explains Agentic AI with one word- “proactiveness”, explaining it as follows: 

“It refers to AI systems and models that can act autonomously to achieve goals without the need for constant human guidance. The agentic AI system understands what the goal or vision of the user is and the context to the problem they are trying to solve.” 

As such, Agentic AI signals towards a major advancement in human-computer interaction by creating a collaborative environment for humans and specialized task force AI agents that can adapt and learn to complete complex, sequential enterprise tasks. To understand this psychologically, agency refers to the ability to take conscious steps to complete a designated task.  

The **Difference between Agentic AI and Generative AI**

Agentic AI differs from Generative AI models like ChatGPT largely by its autonomous task execution and decision-making capabilities. Mentioned below are the core differences between agentic AI and generative AI:  

1. Primary Function: Generative AI produces content based on inputs, while Agentic AI makes goal-based decisions with contextual understanding.  

2. Prerequisite: Generative AI relies on human prompts, while Agentic AI can attain preset and specialized goals with minimal human intervention.  

3. Task Scope: Generative AI can produce content and traditional AI can automate simple tasks, but Agentic AI can independently take action for completing a complex sequence of activities.  

4. Learning: Generative AI learns from existing data while Agentic AI improves with experience and incorporates reinforced learning.  

**Agentic AI in Business**: How Does It Work?

Agentic AI works through “agents”, which are autonomous software components that can learn from massive data volumes and user behaviour to improve performance over time. These autonomous AI agents use advanced AI capabilities and technologies like natural language processing (NLP), machine learning (ML), reinforcement learning, and knowledge representation to learn, reason, and act according to the specific goal they have been designed for.  

Reinforcement learning enables AI agents to adapt to real-time data and feedback. These intelligent autonomous agents are engineered to learn and automatically adjust their actions for their individual tasks based on their learning. As such, they make AI-driven business operations more efficient in high-paced, dynamic, and unpredictable business environments. For example, an AI agent in an enterprise’s supply chain network can learn from real-time weather updates, customer demand, behaviour, market trends, and supplier performance to adjust its strategies and devise the best way to meet its preset goal- minimizing shipping delay, operational costs, and improving customer experience.  

Machine learning enables AI agents to convert vast chunks of raw data into actionable information by learning from data and making predictions on the basis of identified patterns. This helps enterprises take accurate, data-driven decisions using vast chunks of disparate data from various sources- spreadsheets, CRMs, and more.  

Agentic AI typically follows a five-step process to solve dynamic business problems and complete the task it is designed for:  

1. Perceive: Intelligent agents in AI convert data into actionable insights by accumulating and decoding information from different sources and identifying meaningful patterns relevant for their task.  

2. Reason: The reasoning process for understanding tasks and devising strategies or solutions for specialized tasks is facilitated by a large language model (LLM).  

3. Act: Autonomous AI agents execute tasks through APIs that connect them with external systems and ensure compliance with built-in guardrails.  

4. Learn: AI operational efficiency is achieved through continuous improvement of agents, that get better with every interaction and experience.  

5. Collaborate: Open-source agent builder tools simplify agent deployment, enhance the platform’s ability to keep pace with innovation, and accelerate development for keeping businesses at the forefront of innovation.  

**Agentic AI Workflows**

Agentic AI workflows differ from traditional automation like RPA (dependent on preset rules) by using processes where autonomous AI agents decide, take action, and complete complex tasks without requiring constant human intervention. With the ability to learn from feedback and real-time data, agentic AI workflows empower enterprises to unlock their complete potential with AI capabilities that go beyond routine and simple tasks. With reinforcement learning and the ability to improve with time, agentic AI enables enterprises to take a more flexible and iterative approach to break down complex problems and inform their strategies with real-time data.  

As such, Agentic AI takes AI-driven innovation to the core of an organization and empowers it with intelligent systems or “agents” that can learn with every experience, interaction, and strengthen its fabric to improve at pace with dynamic demands and the context of doing business. Reducing or minimising the reliance on human intervention for task execution frees human intelligence for tasks unmanageable without it.  Mentioned below are the core components of agentic workflows:

1. AI Agents: A program or system capable of performing the task it is designed for autonomously, learning, and strategizing based on real-time data.  

2. LLMs: Large language models form the core of AI agents and facilitate natural language processing and generation.  

3. Tools: Tools like APIs and external datasets help LLMs acquire information beyond the training data and tailor agents for specialized tasks.  

4. Prompt Engineering: Agentic workflow automation relies on the quality of the prompt given, as agentic AI builds on the creative capabilities of generative models, and prompt engineering helps generative models process a wider range of queries more efficiently.  

5. Feedback Mechanisms: These help enhance the agent’s decision-making process in alignment with the desired output and specialized task.  

6. Multiagent Systems (MAS): Going beyond simple use cases like automating regular tasks requires enhanced problem-solving by breaking down the problem and distributing it within MASs, comprising different agents. Collaboration between different agents ensures no two agents learn the same information, and each one can be designated a different area of expertise.  

7. Data Integration: Complete AI operational efficiency can be achieved by integrating AI agents into existing infrastructure and creating a consolidated database that the agent can access.  

**Agentic AI Architecture**

Agentic AI architecture supports and automates AI models within the agentic AI system. Its most important advantage is domain-specific organization of agents, resulting in specialization for IT, HR, Financing, Operations, Engineering, and more departments. and discussed below are the main agentic architecture system types: 

1. Single Agent Architectures  

These architectures include a single AI agent that functions autonomously to perceive its environment, devise strategies, and take action to meet its goal. It functions autonomously, without any human intervention or interaction with other agents. Its major benefits include the ease of development and deployment, and cost optimization as compared to multiagent systems.  

2. Multiagent Architectures  

In multiagent architectures, each agent specializes in a specific area or domain and can adapt its role with evolving and complex tasks. Multiagent architectures are flexible and enable the enterprise to respond to a dynamic market seamlessly, leveraging different capabilities of AI (one agent can specialize in NLP, the other in computer vision) and working in collaboration to complete complex tasks.  

3. Vertical AI Architecture  

Vertical architectures include a hierarchy of AI agents, where one leader supervises decisions and other agents report in a centralized manner ensuring complete alignment on task objectives. This hierarchy makes them perfect for sequential tasks, such as workflow automation.  

4. Horizontal AI Architecture  

Horizontal AI architectures involve a peer collaboration model, where AI agents can collaborate and take group decisions in a decentralised manner. Distributed collaboration enables AI agents work parallelly and solves dynamic problems.  

5. Hybrid AI Architecture 

Task requirements determine leadership in hybrid AI architectures, which combine leadership and collaboration. As such, they combine the strengths of hierarchy and distributed collaboration and are perfect for tasks involving strategic teamwork.  

**Agentic AI Use Cases**

Enterprise AI solutions with intelligent agents can revolutionize operations like never before, going beyond the surface-level efficiency of automating repetitive tasks to establishing enterprise-wide intelligence with an autonomous digital workforce capable of improving with time. Discussed below are its real life use cases:  

1. IT Operations 

Agentic AI can drive developer productivity and efficiency by assisting them for tasks like troubleshooting and deploying bug fixes autonomously. It can cater to specific requirements in IT process automation including infrastructure management, system performance optimization, minimizing operational risks, and improving cybersecurity by monitoring real-time data and taking proactive steps to meet its preset task specialization. Nasa’s ROSA, an AI agent built on the LangChain Framework using natural language queries, helps developers interact and understand robots. It is an active tool in NASA’s Jet Propulsion Laboratory.  

2. Customer Experience  

Understanding Agentic AI use cases in customer service and experience enhancement is highly beneficial given the evolving complexity of understanding customer queries and overwhelmed human customer service representatives managing these queries manually. Unlike traditional chatbots that deliver answer answers based on predefined responses, agentic AI chatbots can learn from customer interaction over time, recall relevant data in real time, and anticipate future possibilities of events to give customers accurate responses based on the contextual and sentimental analysis of their queries.  

3. Software Development  

AI task automation with autonomous agents can help enterprises improve code quality, maximize developer productivity, and help them manage complex tasks like code reviews and incident responses more efficiently. Additionally, AI agents can accelerate coding by generating code from simple descriptions, optimizing tests, and ensuring that new changes do not affect existing code.  

4. Financial Services  

An article by the World Economic Forum affirms the transformative potential of agentic AI in financial services. Be it workflow optimization, customer service enhancement, compliance management, or data-driven decision making, agentic AI is poised to revolutionize the world of banking and financial services with its ability to manage and understand vast data sets from diverse sources, respond to fast-paced dynamic environments, and identify meaningful patterns to minimize potential threats.  

5. Healthcare  

AI operational efficiency with intelligent agents can help healthcare providers focus and prioritize personalised care, delegating resource-intensive repetitive tasks like billing and scheduling to AI agent assistance. Additionally, AI agents in healthcare can improve every touchpoint of interaction between patients and healthcare providers to improve the efficacy of treatment outcomes with their ability to handle large datasets, identify meaningful insights, and take adequate action to solve a complex administrative problem before it escalates to a major barrier in healthcare delivery.  

6. Supply Chain Management  

AI task automation with autonomous agents is particularly beneficial for supply chain management as it can minimize the manual effort required for supplier management, identify the right supplier based on the desired selection criteria like cost effectiveness, cross-reference supply chain operational processes against real-time data, and offer actionable insights to enterprises from centralized data and enable them to make better decisions on the fly as well as in the long run.  

7. Sales  

Enterprises can revolutionise their sales processes by integrating agentic AI into their existing CRMs, extracting actionable insights from past customer interactions, and maximizing lead generation efforts for customers who are most likely to convert. Agentic AI can nurture leads by autonomously communicating with them based on contextual understanding, offer personalised pitches, managing multiple leads simultaneously with relevant approaches.  

**Benefits of Agentic AI for Large Enterprises**

Agentic AI solutions for enterprise automation empower modern-day businesses the following core benefits that position them as flexible, resilient, and future-proof industry leaders capable of greater innovation:  

1. Interoperability: AI operational efficiency can be maximized if different systems and modules talk to each other and collaborate seamlessly, giving the enterprise a unified digital transformation with central intelligence from all its parts.  

2. Modularity: This refers to breaking down complex problems into smaller and specialized modules designed for specific actions. Modularity contributes to resilience in enterprise AI solutions by enabling enterprises to adapt to changes flexibly, simplify development, and upgrade without affecting the entire system.  

3. Reinforcement Learning: This capability helps systems to learn from their environments, real-time data, and feedback to improve decision-making over time and remain at par with evolving user needs and facilitate prompt enterprise response.  

4. Scalability: Distributed and scalable computing power helps AI agents manage increasing data load and respond to dynamic, evolving, and unpredictable demands without compromising enterprise agility.  

5. Specialization: Agentic AI solutions for enterprise automation go far beyond traditional, surface-level automation systems. Designed to complete tasks to their last granular detail, AI agents can facilitate seamless workforce specialization and division of tasks as enterprises face the challenge of workforce shortage or finding the right skill for specialized tasks, driving greater efficiency and innovation.  

6. Conscious Experimentation and Innovation: Agentic AI can think, strategize, learn, and take action to complete complicated, multi-step tasks. At its core is the ability to drive innovation, learning from doing, and informing strategies based on the evolving task complexity. One of the major agentic AI examples in this regard is SciAgents, a multiagent model at MIT including robot scientists and a Critic Agent to develop, review, and improve research plans. 

7. Reliability: How agentic AI offers a greater edge over traditional intelligent process automation is through its reasoning, which eliminates the risk of invented information errors. Agentic AI can offer reliable and trustworthy decisions by identifying the most reliable information sources and aligning their decisions with company goals.  

Conclusion

To simply answer what’s agentic AI, it is best to say that is the revolutionary wave of AI innovation building on top of previous AI models and offering greater capabilities of autonomous action, reasoning, learning from experience, and making decisions. Having understood how and why it can revolutionize enterprise operations, it is crucial to understand that every enterprise will need tailored agentic AI implementation for maximum efficiency, considering unique strategic objectives and goals from implementation. Get in touch with us today to share your enterprise goals and revolutionize how you work to meet those goals with agentic AI! 

FAQs

1. What does Agentic AI mean? 

 Agentic AI refers to AI systems that are capable of working autonomously without requiring constant human intervention, learning from experience, and understanding vast data sets for taking decisions and completing their assigned, multi-step tasks.  

2. How to build agentic AI? 

Building agentic AI requires a deep understanding of organizational objectives, infrastructure readiness, implementation challenges, and goals from specializing respective domains. To minimize disruption of existing processes, it is important to identify areas of highest impact and tailor strategies to help organization’s members learn continuously and adapt to change.  

3. What are some top Agentic AI frameworks? 

 The choice of the right framework is necessary to build, deploy, and manage AI agents seamlessly. Top frameworks include:  

LangChain: It is an open-source framework for LLM applications. It employs a modular architecture that simplify LLM management with necessary abstractions and complex concepts.  

CrewAI: This is an open-source orchestration framework for managing multiagent solutions. It has a role-based architecture, where agents are treated as crew workers with specialized task roles.  

AutoGen: This is an open-source framework from Microsoft that follows a three-layer architecture useful for creating multiagent AI solutions that can solve complex problems.