10 Agentic AI Use Cases

Agentic AI is probably the hottest topic in the world of enterprise IT. In fact, software vendors in just about every niche have added agentic AI features to their products over the past couple of years.

However, in the real world, adopting AI agents isn’t such a straightforward process. On the one hand, many teams struggle to determine the right tools for their needs in a fast-evolving market.

On the other hand, in their rush to roll out solutions, some companies lose sight of the need for provable ROI.

Today, we’re charting a course through an important prerequisite to successful adoption by checking out some of the most prominent agentic AI use cases.

We’ll start by giving a brief overview of what AI agents are and how they work, before presenting 10 of the most common use cases, clustered into functional categories.

By the end, you’ll have a more in-depth understanding of the concrete ways that agentic AI can add value across departments and teams.

Let’s start with the basics.

What is agentic AI?

An AI agent is an LLM-powered software solution that can autonomously assess information, perform reasoning, and take actions in pursuit of a goal. The idea is to replicate and automate tasks that would otherwise require a human to perform.

In other words, agents accept inputs and queries, use context to determine what needs to be done, and trigger the appropriate actions to provide a resolution.

Inputs can be natural language queries from end users, system events, or even outputs from other AI agents. An LLM then determines the overarching goal of this and uses reasoning, along with any additional context we expose it to, to break this into constituent subtasks.

These are then carried out via connected tools and functions, with the response from these fed back to the agent for downstream processing or feeding back to the end user.

While some AI agents act entirely autonomously, many solutions also retain human interactions. These are referred to as Human-in-the-Loop systems and may include approval steps for certain actions.

For example, we might enable an agent to draft a response to a support query, but give a human service desk colleague the final say on whether or not to send this.

In this way, AI agents provide a range of benefits, including driving efficiency, accuracy, consistency, and resilience within internal processes.

You might also like our in-depth guide on digital workers .

10 most prominent agentic AI use cases

Now, with a firm grasp of what AI agents are, we can begin to think about some of the core ways that businesses are using them in the real world.

As we said earlier, we’ve grouped these into a few key functional themes. That way, we can more easily assess our needs across similar processes and tasks.

So, let’s jump in.

Internal services

First up, many of the most prominent uses of AI agents today fall under the umbrella of internal services. Broadly, these are defined workflows that end users can trigger to access specific outcomes and resources from departments such as IT, HR, finance, or facilities.

They also have a few key characteristics that make them prime candidates for agentic AI.

Firstly, these are typically triggered by requests from colleagues who may not have a high level of understanding of the underlying concepts, procedures, or issues.

Secondly, once the nature of the request has been established, the actions required to resolve the issues can often be relatively consistent and standardized.

Let’s check out some examples of how this can work in practice.

1. IT service desk agents

IT service desks are perhaps the most widespread example of an agentic AI use case. This is also probably the use case with the most mature domain-specific, dedicated solutions for building agents.

Service desks deal with large volumes of varied requests, ranging from routine tasks to more complex, situation-specific incidents and issues. This will often also require a plethora of tools and services to manage.

As such, it is highly suited to agentic AI.

One common example of this involves deploying AI agents to triage all incoming tickets. So, users can submit requests via a single interface. The AI then determines the category, severity, and urgency of the underlying issues.

More importantly, it determines if this is something it can resolve itself or not. For example, simple, well-documented tasks such as password resets could be handled autonomously, but more severe, complex issues like security incidents would likely be escalated to a human agent.

In this way, we can reduce the burden of routine tasks on service desk colleagues, freeing them up to work on more challenging tasks, while also providing faster, more efficient resolutions for end-users.

2. HR agents

HR is another internal vertical where AI agents can greatly reduce workloads around simple, repetitive tasks. However, the challenge here is somewhat different than we’d see in the case of IT.

Part of the reason for this is that many of the tasks an agent would perform are considerably more straightforward. These may involve triggering simple workflows, like submitting a vacation request.

Alternatively, many interactions will take the form of basic information requests. In other words, responding to user queries related to internal policies, procedures, or entitlements, often in the form of a chatbot.

This requires adequate natural language processing capabilities to determine the specific information that the user requires. The agent can then use this to trigger a Retrieval Augmented Generated (RAG) action to retrieve policies, and synthesise them into a resoinse.

Where necessary, the agent might then trigger relevant actions on the basis of this. In turn, this saves time for HR colleagues and end-users alike, as well as providing a more streamlined experience for handling queries.

3. Finance agents

Finance is one of the most tightly regulated internal departments. It also encompasses a range of processes, from basic approval workflows to more sensitive, mission-critical tasks.

However, the most common agentic AI use cases for finance teams often relate to managing interactions with the wider organization. Usually, this means applying defined logic to process submissions such as expense claims or budget requests.

The real benefit of AI agents in these cases is that they are able to process requests where the information submitted is sufficient to do so, or escalate to human colleagues where it is not.

For example, say we have internal rules that expenses under a certain threshold or from specific vendors can be automatically approved. Actioning this requires the right information to, first, be present, and, second, adhere to these rules.

So, depending on the submission, our agent could process an approval, escalate to a human user, or follow up with the requestor to ask for more information.

Crucially, these decisions can be taken autonomously, giving us a vehicle to more easily implement complex, conditional logic than would be possible with hard-coded automations.

4. Enterprise-wide assistants

Our last agentic AI use case for internal services is a slightly different prospect. Rather than focusing on a single department or function, more and more organizations are rolling out unified experiences for access employee services.

This simultaneously enhances end-user experiences while providing exceptional levels of centralization, oversight, and interoperability across internal services.

Typically, this takes the form of an enterprise chatbot . This means using either a dedicated UI or an existing communications tool to accept natural language triggers from end users. The difference, however, is that these could relate to any internal policy, process, or workflow.

This is particularly important given that, in many cases, users won’t know which department’s mandate their issue falls under.

From an architectural standpoint, this can be somewhat more complex than function-specific agents, though. That is, under the hood, users may need to interact with multiple function-specific agents via a single interface.

Each of these will be exposed to different tools and data, as well as having tailored capabilities in terms of their reasoning and logic.

As such, we might require additional components, including dedicated planning agents as well as tools for orchestration within multi-agent systems.

Take a look at our comprehensive guide to AI agentic workflows to learn more.

Customer-facing services

While internal services are some of the most prominent agentic AI use cases, plenty of organizations are rolling out agents to external-facing workflows too.

Indeed, in an age of shrinking budgets and increased competition, many brands are turning to novel solutions to simultaneously boost efficiency and customer satisfaction rates.

5. Customer service agents

Perhaps the most obvious opportunity here is applying the principles and techniques we saw in the previous section to interactions with customers.

So, just like with internal service users, we can greatly enhance experiences for prospective and existing customers by providing them with unified experiences for resolving their queries. This comprises both information retrieval and triggering resolution actions.

To understand this, it’s helpful to consider the kinds of tasks that are most often handled by human service desk agents. Some of the most routine queries here include seeking updates on orders, information on returns processes, or clarification on product details.

As we saw earlier, the challenge here is primarily enabling users to submit natural language queries, which agents can interpret to determine the users underlying requirements, and return the appropriate information or take actions, as necessary.

However, this introduces additional elements, as users are more than likely going to want to return information relating to their specific order or case.

As such, these kinds of agents require appropriate controls to ensure that customer data can be retrieved securely, including authentication and authorization.

6. Sales prospecting agents

Sales is another area where we can greatly reduce the burden of manual admin tasks by implementing agentic AI. On the one hand, we could approach this in much the same way as we saw with customer service, providing information to leads via chat interfaces.

However, we can also think about more sales-specific use cases, across prospecting and lead qualification.

Most large organizations have rules in place to score incoming leads, according to factors including their employee headcounts, industry, or revenue. The tricky thing is, the more information we ask from users, the less likely they are to complete a form, generating a lead.

One exciting use case for agentic AI is enabling users to provide a minimum amount of data, with agents performing enrichment on this to provide the remaining values that we need to score leads.

The key thing here, compared to traditional automation flows, is that agents can determine how to get this data for themselves. Consider the fact that not every business has an up to date website or LinkedIn profile.

AI agents can attempt to populate values from a range of publicly available information sources in sequence, in a way that would be very challenging with a basic automation script.

Software development

As you might expect, some of the most prevalent agentic AI use cases are made by software developers, for software developers.

This is also something of a unique case, given that interpreting and generating code is one of the most important benchmarks for large language models, with certain models focusing entirely on performing well on coding tasks.

Because of this, there are some highly exciting opportunities for augmenting and assisting human developers.

7. Code generation agents

Firstly, there are code generation agents. As you might expect, these accept natural language inputs and output working code. However, there are a couple of additional elements to this that make it an agentic rather than solely generative AI system.

For one thing, coding agents that are going to be used in production are most effective when they have access to our existing codebase - or relevant sections of it, at least.

Of course, we could simply provide this as training data within a generative system. What differentiates an agentic system is the ability to contextually determine which parts of an existing codebase are relevant and retrieve them via RAG as and when required, in real time.

More importantly, code generation is most effective when the system is capable of assessing its own output and making changes independently. This requires what’s known as an iterative refinement pattern.

Broadly speaking, this means designing an agent that generates an initial output, checks it for completeness or accuracy, and makes adjustments to improve it.

This empowers agentic systems to generate code much more effectively than a standalone LLM could, including learning from previous executions to better understand future tasks.

8. QA and testing agents

Another key use case for agentic AI within software development workflows is performing QA and testing tasks.

Whereas automated QA has been around for a long time, agents introduce more advanced capabilities, including autonomously identifying, designing, and executing tests.

This much more closely mimics the actions of a human tester, rather than statically executing pre-defined tests for all code.

In the first instance, this relies on the agent’s ability to understand the code itself. This allows it to determine the expected outputs, interpret the approach taken, and plan suitable testing requirements.

Agents can then act autonomously on the basis of this to trigger predefined test flows or design and execute their own. Crucially, they can also interpret the outputs of these in order to make changes to the code, feed results back to users, implement further tests, or sign off the code.

In this way, we can greatly expedite the development process by reducing bottlenecks that emerge from overburdened testing teams.

Workflow automation

Lastly, we have agentic AI use cases that mimic the core actions we might otherwise take in traditional workflow automation tools. With the increasing proliferation of agent builder tools, more and more teams are employing agents to build efficiency within day-to-day workflows.

Combining simplified development processes with more advanced, autonomous reasoning and logic, agents are fast becoming a core part of automation strategies across all kinds of teams.

Here are two of the most prevalent use cases.

9. Document processing

Document processing is one critical area where traditional workflow automation solutions often fall short. The challenge here is that we’ll often need external Optical Character Recognition (OCR) tools, on top of whichever workflow platform we use to handle automation logic.

This introduces additional complexity in terms of building, maintaining, and using solutions.

Agentic AI offers a clear alternative, leveraging models that are capable of interpreting documents and extracting key values, without the need for a standalone OCR solution.

This approach also offers us greater flexibility, as LLM-powered document processing is less reliant on fixed formats or wording for submitted documents, since the system can independently reason to infer values.

Extracted data can then be used to perform further reasoning, actions, and processing in order to reach the goals of a document processing workflow. For instance, approving an invoice or passing a contract to a human colleague for review.

10. Integrating SaaS tools

Another core use case for traditional automation tools is configuring and maintaining integrations across SaaS platforms. Essentially, this means sending data from one tool to another via HTTP requests, in response to defined events.

Obviously, this is a highly effective approach, but it also has some important limitations. Most notably, triggers and actions are basically static. We can add conditional logic, but this must also be predefined.

This can create several problems, including if the API spec of a connected tool changes. When a request isn’t executed as expected, the whole flow might fail.

Agentic systems can provide a couple of key benefits here. Rather than solely relying on defined events and API requests, we can leverage more advanced tools, enabling agents to autonomously select, populate, and trigger requests.

For instance, many agent tools now support MCP servers, enabling them to interact with connected tools for a wide range of supported actions.

In turn, this means that we’re less impacted by changes to individual endpoints within a vendor’s API, as well as reducing the development burden required to output solutions, as we don’t need to manage individual APIs manually.

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