What if you're running a healthcare organization, and you need a 24*7 assistant who knows your patients better than YOU, digests complex patient data and then provides personalized treatment plans?
Sounds impossible?
But Agentic AI does all that.
Standard LLMs can respond, but they won't act autonomously; they give you advice but never execute the work, but agentic AI turns “say to do”.
You'd ask the AI agent – hey, this is what we need you to do; these are the parameters: connect to other agents and simplify complex workflows. The agent will apply the logic and use the reasoning capabilities to perform the task by taking on different roles each time.
Sometimes, it can work as a chef agent, sometimes as an SQL agent and sometimes as a content agent for you. You name the role, and it prioritizes the tasks and achieve the objectives.
Best part? Zero human intervention.
Let's talk about what is agentic AI is, its types, possible use cases and how we can help you build an intelligent application for your business.
60 Second Summary
1) What is Agentic AI: Agentic AI refers to intelligent systems that act independently, make decisions, and execute tasks without human input—unlike standard LLMs that only generate responses.
2) How Agentic AI Works: These agents perceive their environment, reason through data, take action via APIs or systems, and keep learning from feedback using advanced techniques like reinforcement learning.
3) 5 Key Types of Agentic AI Agents:
Simple Reflex Agents: Rule-based bots
Model-Based Agents: Remember past interactions
Goal-Based Agents: Take decisions to achieve future objectives
Learning Agents: Continuously improve from experience
Utility-Based Agents: Optimize actions for best results
4) Practical Use Cases Across Industries: Agentic AI is transforming healthcare, finance, manufacturing, customer service, HR and cloud operations.
5) Why It Matters for Business Leaders: Agentic AI helps CEOs reduce costs, automate workflows, scale operations, and deliver better customer and employee experiences, acting as a strategic, self-operating partner.
Agentic AI means multiple agents work autonomously to perform complex tasks with no human required.
Though Traditional AI systems such as ChatGPT or Bard can perform simple tasks for users, when it comes to solving complex problems, these models can't adapt, learn and make decisions in real-time. They struggle with complex, multi-step problems.
The only difference between agentic AI and rule-based LLMs is that these autonomous AI agents have one primary focus – making decisions such as increasing business sales, reducing logistics costs or maximizing business revenue.
In marketing, AI agents provide insights and help marketers plan their campaigns, while in the supply chain industry, it can save hundreds of millions of costs.
AI Agents Examples:
Take an example of customer service where multiple AI agents work in sync with each other; one agent can search for knowledge bases; another agent can understand the context of the conversation and a third agent can solve customer issues.
Think of AI agents as a multi-talented performer that can plan your travel tips, make travel plans, and sometimes, if required, provide virtual support to older adults.
In a nutshell, multiple AI agents will work independently, learn from previous interactions and work alongside humans.
What Does The Working of Agentic AI Systems Look Like?
Here's the 4-step process that every Agentic AI system follows:
Agentic AI observes the environment just like the constructor does the site surveillance, understanding the problem, analyzing the requirements, studying the existing code, and documentation.
This autonomous decision-making model learns on its own through sensors, connected technologies, APIs and user inputs.
Consider the example of customer support Agentic AI-based system where the customer asks the LLM where my order is.
So, it will extract data from multiple sources, like it would go to the ecommerce order database to track the order status, then it can go to logistics API to find the real-time path, and it then considers the customer inputs to provide personalized answers to users.
2) Use The Reasoning Capabilities
After data collection, these intelligent agents use natural language processing capabilities to understand the context of the conversation, analyze the data and then give meaningful insights.
The agentic AI acts as a reasoning engine by breaking down the problem into small and manageable tasks by interacting with other LLMs such as GPT-4, Claude and RAG models to enhance the accuracy of output.
3) Execute The Action
In this phase, agentic AI integrates with other external APIs, third-party models or systems to respond. It can do multi-step workflows and act.
4) Learn
While responding, AI agents never stop learning as they take continuous feedback from the users and then adjust their responses accordingly.
It uses different techniques, such as reinforcement and self-supervised learning, to improve future responses.
What Are The Most Popular Types of AI Agents?
Here are the few most popular types of Agentic AI applications, which are as given below:
Simplex AI agents work on predefined rules and formats to solve the user's query. They take input from the user, follow the rules and provide the responses based on their memory.
For instance, if the temperature drops to 18 degrees Celsius, then turn on the heat. That's the condition-based rule that these simple chatbots follow.
These types of chatbots don't rely on past interactions and are not trained on learning processes; they just provide you with the output based on predetermined rules it has been trained on.
They generally react to environmental changes through sensors such as temperature, light, etc, and the actuators involved in the agent execute tasks.
Email autoresponders, which send the reply to the user by identifying the specific keywords or phrases the agent has been trained on. That’s simple reflex AI agent.
These are the updated versions of simplex reflex AI agents, as these models store information about past events and interactions.
This AI agent works like an intelligent assistant to help users, from making reservations to remembering their past preferences and providing them with a better response each time, as it can react to partially observable environments.
Unlike other agents, learning agents keep on learning new experiences and updating their knowledge base, and that's how they improve their performance.
Best part? These next-gen AI platforms don't rely on programmed rules; instead, it learns from feedback and experiences to adjust their responses.
The best example of learning agents is customer service chatbots, which improve their responses based on interaction outcomes.
If we talk about the e-commerce sector, learning agents are all the way around. A user lands on the site and receives personalized product recommendations each time, which is possible through intelligent algorithms.
That's possible as the agent keeps track of user preferences and their shopping history.
Result = Better response each time (continuous learning)
5) Utility-Based AI Agents
These AI agents don’t just work to achieve goals; they focus on one thing – maximize utility, which can be reward in this case.
They use the utility function, calculate the score of each utility value and find the score of each possible action. The agent then decides the best possible action based on the highest utility (shortest time, quickest route), whichever might be the case.
When To Use?
Utility-based agentic systems are used for complex scenarios which require creating a balance between multiple objectives.
These artificial intelligence programs work best when considering different investment options, determine the risk and deciding the best investment scheme for the user.
They can work in complex environments and can predict the optimal route based on utility score and the set goal.
What Are The Five Major Applications of Agentic AI Systems?
Here are a few use cases where AI agents play a significant role in every industry, be it retail, e-commerce, manufacturing, supply chain, etc.
1) Agentic AI For Healthcare
While generative AI has been there for a while, it's restricted to providing responses to patients. Agentic AI has got a significant upgrade.
These innovative healthcare scheduling systems don't just monitor patient data, detect anomalies, book follow-up appointments, and suggest changes in treatment plans.
This not only reduces clinician workload but also provides personalized care to patients through its ability to digest loads of healthcare data. Thus, there will be optimal utilization of resources and time as administrative tasks are now automated.
These systems know more than a healthcare provider as it has a record of everything, such as genetic, lifestyle and environmental data.
Today, it performs the role of 24*7 healthcare monitoring assistant when integrated into wearable technologies and can alert doctors and patients before they become a serious concern afterwards.
2) Agentic AI For Financial Processes
Unlike traditional AI systems, Agentic AI shifts the focus from automation to being autonomous, meaning "do it for me economy".
According to Microsoft, the financial sector is the leading adopter of AI as compared to other industries such as retail, healthcare, industrial manufacturing, etc.
These agents use sensors and connected technologies for:
Making real-time trading decisions
Automation of routine tasks
Providing personalized offers as per the client's profile
Enhancing customer engagement through virtual financial assistants
For instance, In algorithmic trading, an agent is there who makes independent decisions to buy or sell a particular stock with no human support required.
This increases the profitability of financial companies as they don’t need to hire humans to take up calls related to buying and selling assets. So, it will save costs.
This means Agentic AI platforms change the way how financial companies conduct their daily operations and administrative work, such as compliance checking, data entry and transaction processing.
When AI can automate everything, this frees up the human staff to focus on the decision-making part.
3) Agentic AI For Manufacturing
Agentic AI, when applied in the manufacturing sector, doesn't require human supervision and can predict equipment failure 72 hrs in advance so that the production process won't be disrupted.
They can get their machinery recovered during the low production phase.
Such predictive maintenance ensures that there will be no production delays and can manage demand fluctuations early on.
4) Agentic AI For Customer Experiences
As customer expectations are changing and they want instant gratification for everything, so do companies need to change the way how they interact with them.
A study from Forrester states that companies need to spend 5x the cost for acquiring a customer, which is way more than retaining the existing customer.
That means to retain your existing customers, you need to provide them with faster and more personalized responses, so they won't go away to another brand/ switch to another product.
Let's say you’ve millions of customers to serve every day, and none of your customer support staff is free, and you saw that customer complaints have increased to an extent; in such a case, you'd deploy 100 million agents for those customers.
Those agents guide them along the way, doing sales, marketing and relationship-building roles. This means when an agent is deployed at every step of the customer journey, they would be satisfied on the first call.
julep.ai is a platform for building AI agents that can handle multi-step workflows ranging from providing personalized content to users to creating agentic chatbots that can schedule appointments or do constant follow-ups.
5) Agentic AI For Cloud Optimisation And SecureOps
As cloud environments grow more complex and AI adoption expands, businesses are facing increasing challenges in managing cloud costs and securing their infrastructure — all while striving to move fast and build sustainable AI systems.
A study by Flexera reveals that over 30% of cloud spend is wasted, and security misconfigurations continue to be one of the leading risks in cloud-native operations.
To operate efficiently, organizations must continuously optimize cloud spend and proactively mitigate security risks — without slowing down their teams or relying on manual processes.
Now imagine you're running workloads across multiple cloud platforms, and your FinOps or security teams are stretched thin. Inefficiencies begin to pile up — idle resources, over-provisioned instances, outdated access controls. What if, instead, you could deploy autonomous AI agents to manage each of your cloud accounts?
These agents work around the clock — monitoring, recommending, and even executing actions to reduce costs, eliminate risks, and maintain compliance — without requiring constant human oversight.
Costimiser is a platform designed to build agentic AI for Cloud FinOps and SecureOps. It enables autonomous agents to manage everything from cost optimization and budget control to security risk detection and automated remediation, using tools like RailGuard for enforcement.
With Costimiser, you don’t just get insights — you get autonomous action. It’s a purpose-built FinOps Agentic AI platform for autonomous multi-cloud cost optimization and security governance at scale.
6) Agentic AI For Human Resources
Just like other industries, HR-focused AI agents are no exception. As human resource professionals spend most of their time on administrative processes, they won't be able to provide quality time to make employees more satisfied.
But with intelligent agents, they can screen resumes, calculate individual scores, schedule their interviews and provide personalized support to them.
At the organization level, HR support agents can recommend training plans based on each employee's skills and can automate administrative tasks such as managing leave requests or answering employee questions.
This means hiring has become a daunting and time-intensive task, and to take the red pill away, LinkedIn introduced its own AI agent, "Hiring assistant”, to save recruiter hours so they can spend more time on people-centric tasks.
Screening candidates, calling them, and scheduling their interviews take up to more than 20 hours/ week, which itself is a significant number, and this AI agent is meant to make the hiring process smoother.
What Benefits Does Agentic AI Provide For CEOs?
Here are the few benefits of Agentic AI for busy CEOs and CXOs:
1) Become Your Strategic Partner
The difference between standard LLM and Agentic AI is that it helps business owners like you make decisions proactively. It's not confined to responding; instead, a major part of their work is to act.
They provide a bunch of information to CEOs, such as analysis of customer behaviour, competitive trends, and consumer preferences. These insights serve as a goldmine for any business owner as they can now bridge the gap between their vision and market expectations.
For instance, The Agentic AI system predicts an increasing hype of eco-friendly products in a specific region, and then the CEO can think of serving customers through sustainable initiatives in that region.
They will then plan and consider the risk and reward factors to see whether they can get customers from that region or increase their sales.
2) Providing Personalized Experiences To The Customer
Today, customers don't want brands to serve them in a one-size-fits-all approach. They want personalized experiences that are unique to their preferences.
Agentic AI does a pretty good job of understanding customer insights and analyzing their behaviour and their social media activity.
Such detailed insights help them personalize their shopping experiences, such as recommending products based on their browsing history and their preferences.
Not only that, but these agents can also even track that ABC customer frequently abandons the cart because of high delivery charges, so the next time, the CEO or CMO tries to retarget the same customer by providing a free delivery option.
3) Scalable For Growing Enterprise
Rule-based LLMs differ from agentic AI in a way that they can handle multiple tasks simultaneously as you can connect with different tools and APIs.
Like in the case of ecommerce, they can manage your additional workload, provide quick support to customers, especially during the peak season and free up your human staff to focus on essential business areas.
4) Cost Reduction (No More Extra Hires)
Often, B2B CMOs are overwhelmed with meeting their revenue goals and, at the same time, concerned about increasing hiring costs.
In account-based marketing, you need more people, such as marketers to create campaigns, analysts to do account segmentation and an operations team to manage the data.
Now, account-based marketing has been replaced by agentic-based marketing wherein AI agents can do the heavy lifting work such as tracking leads, identifying buyer's journeys and then retargeting that customer by running campaigns.
Suppose a customer lands on your website and visits your pricing page; then the AI agent can alert the sales team to reach out to that prospect, send a follow-up email automatically and retarget them by running an ad.
Result – Smaller team = Cost savings = More demos = More sales cycle
How Can BigOhTech Help You With Developing Agentic AI For Your Business?
At BigOhTech, we can create agents for any industry that can perform multi-step workflows without compromising on quality and will never hallucinate.
We offer full-cycle AI development services that start with creating an AI strategy, developing an agent, deploying it and providing round-the-clock support.
Our talented AI engineers have developed innovative solutions for businesses in diverse verticals such as aviation, hospitality, energy and power, retail, ecommerce, logistics etc. This means they have cross-industry experience in creating custom and prebuilt AI agents.
Best part? We don’t stop after deployment that’s why we provide ongoing maintenance and model training to ensure that your AI agent remains updated with the current data patterns.
