What is Agentic AI? Key Types and Use Cases
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Imagine a customer’s order is delayed. They contact your support team, but in order to resolve the issue - you need to check the order status, contact the logistics provider, review the delivery policy, and then decide what to do next.
Now imagine an AI system that can coordinate these steps. What if it can gather the required information, determine the appropriate next action within your business rules, and arrange a solution? If the case requires an exception, it can even escalate it to a human.
In short, rather than automating a single task, the system works toward resolving the entire problem.
That’s the idea behind agentic AI systems.
Agentic AI enables AI systems to pursue defined goals, plan and execute multiple steps, use external tools, and adapt their actions based on the results.
But how does agentic AI work? How is it different from traditional AI, and where can businesses use it?
In this guide, we’ll talk about agentic AI in detail, its types, practical business use cases, and how organizations can build agentic AI systems to solve real-world problems.
Agentic AI is an approach to building AI systems that work towards achieving the set goal, plan tasks, take actions, use tools, and make changes based on the results, with less human help. These systems can operate with a single AI agent or coordinate multiple agents to handle complex workflows, depending on the task.
Unlike AI applications that primarily generate responses or provide recommendations, agentic AI systems can go a step further by taking permitted actions to achieve an objective.
For example, Commerzbank uses an AI agent called Ava to handle customer requests. [1] Ava handles over 30,000 customer conversations every month, resolving around 75% of requests on its own. It helps customers with everyday banking tasks such as blocking lost credit cards, check the transaction backlog for customer facing payment delays and more.
The key difference is not simply that agentic AI uses multiple agents. It is the system's ability to work toward a goal, coordinate actions, and respond to changing information rather than just produce an answer or execute a fixed sequence of steps.
Traditional AI applications | Agentic AI systems |
Respond to prompts or perform specific tasks | Work toward defined goals |
Often follow predefined workflows | Can plan and adjust steps based on results |
Generate content, predictions, or recommendations | Can use tools to act on information |
May require people to coordinate tasks across systems | Can coordinate multi-step workflows with appropriate oversight |
Before jumping into how an Agentic AI system architecture works, it is important to understand who designs and build it.
Agentic architecture is usually designed and built by AI developers or technology providers based on a business's needs. They provide a clear structure or roadmap that helps AI agents understand a task, decide what to do, and take the right actions to complete it.
Once the architecture is clear to AI agents, it follows the core agentic loop: "Think, Act, Observe, and Repeat"
In simple terms, agentic architecture helps AI agents move from understanding a request to taking steps to complete it.
Modern Agentic AI systems can be divided into different types based on how they make decisions, plan tasks, and work towards a goal. Here are the different types of Agentic AI systems with examples:
Reactive agents respond to what is happening right now. They follow predefined rules and take action without remembering past events or planning ahead.
Example: IBM's AskHR started as an automated HR assistant that responds to employee questions and handles routine requests. It can help employees with tasks such as checking leave policies, accessing payroll information, and requesting employment letters. [2]
Note: AskHR has evolved into an agentic system, so it is not a purely reactive agent today.
These agents understand a goal, plan the steps needed to achieve it, and adjust their approach if something does not go as expected.
Example: GitHub Copilot's coding agent can take a developer's task, examine the code, make changes, run tests, and raise a pull request for review. It follows multiple steps to complete the assigned task. [3]
Workflow agents handle structured tasks by following a defined process. They can move between different steps, access business information, and complete tasks with minimal human involvement.
Example: Coca-Cola Andina introduced Andi, an AI-powered HR assistant that helps employees get answers about leave, benefits, and company policies through WhatsApp. If an issue needs HR support, it can create a ticket for the HR team. [4]
Multi-agent systems use multiple AI agents that work together. Each agent handles a specific part of a larger task, while a coordinating agent brings their work together.
Example: LTM developed RAIma, an AI-powered HR assistant that connects employees to different HR services. It uses multiple specialized agents and integrates with systems such as SharePoint and ServiceNow to handle employee requests. [5]
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.
While generative AI in healthcare has been there for a while, it’s restricted to providing responses to patients. Agentic AI systems in healthcare take this a step further by:
This not only reduces clinician workload but also provides personalized care to patients through its ability to digest loads of healthcare data. Thus, resulting in optimal utilization of resources and time as administrative tasks are now automated.
Further, healthcare agentic AI systems know more than a healthcare provider as it has a record of everything, such as genetic, lifestyle and environmental data.
Today, it can even perform 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.
Unlike traditional AI systems, Agentic AI shifts the focus from automation to being autonomous, meaning "do it for me economy".
These agents use sensors and connected technologies for:
In algorithmic trading, an agent independently analyzes market conditions and makes decisions to buy or sell stocks without requiring human intervention.
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 in Fintech can automate everything, this frees up the human staff to focus on the decision-making part.
Agentic AI, when applied in the manufacturing sector, doesn't require human supervision and can predict equipment failure 72 hours 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.
And it's not just about predicting failures. Manufacturers are also using Computer Vision Development to detect defects, improve quality control, and make production lines smarter.
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.
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.
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.
LinkedIn realized lately that hiring managers are overwhelmed with administrative tasks every then and now. Every day, 55% of HR professionals say that their roles demand higher expectations than before.
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. [3]
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.
Implementing agentic AI requires more than developing an AI agent. Businesses need to identify the right use cases, define workflows, integrate agents with existing systems, and establish mechanisms to monitor their performance.
BigOhTech approaches agentic AI development across these stages, from AI strategy and agent development to deployment and ongoing maintenance. Our experience across industries such as aviation, hospitality, energy and power, retail, eCommerce, and logistics provides an understanding of how AI agents can address different business workflows and operational requirements.
However, deploying an agent is only one part of the process. Continuous monitoring, model training, and regular updates are essential to help agents adapt to changing business requirements and data patterns. With the right development and maintenance approach, businesses can use agentic AI to automate multi-step workflows, improve operational efficiency, and support more informed decision-making.
Agentic AI is AI that doesn't just answer your questions but also works toward a goal. It can plan what needs to be done, use different tools, take action, and check whether the task is completed. It can also ask for human help when needed.
A chatbot usually responds to the questions you ask. Agentic AI goes a step further. It can take action on your behalf, such as checking an order, updating customer details, or scheduling a follow-up. It doesn't need you to guide every single step. Find how BigOh tech's Agentic AI Development Process works.
Think of it as a process where AI figures out what needs to be done, breaks the task into smaller steps, and starts working on them. It uses the required tools and data, checks the results, and moves to the next step. If something goes wrong or needs approval, it can bring a human into the process.
Agentic AI comes in different forms, depending on how it handles tasks. Some agents respond to situations as they happen, while others plan their actions to achieve a specific goal. There are also workflow agents that handle structured tasks and multi-agent systems where multiple AI agents work together.
Agentic AI is finding applications across several industries. Healthcare, finance, manufacturing, customer support, HR, and cloud management are some examples. Businesses in these sectors use AI agents to handle repetitive tasks, manage workflows, and reduce manual effort.
Not necessarily. Agentic AI is mainly designed to take care of repetitive tasks and workflows that require multiple steps. This gives employees more time to focus on decision-making, problem-solving, and work that needs human judgment. People can still step in when a task requires approval or special attention.
Yes! AI agents can connect with existing business systems such as Salesforce, SAP, and HubSpot using APIs and webhooks. This allows them to access relevant information, update records, and trigger actions across different systems without requiring employees to switch between tools for every task.
Launching an AI agent is just the beginning. It needs regular monitoring to make sure it's completing tasks correctly and responding within expected timeframes. Businesses also need to update prompts, test workflows, review security, and make changes when APIs, business processes, or data requirements change.
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