# 10 Agentic AI Use Cases May 23, 2025 by Ronan McQuillan 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. ## 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. #### 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. One common example of this involves deploying AI agents to triage all incoming tickets. 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. #### 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. This may involve triggering simple workflows, like submitting a vacation request, or responding to user queries related to internal policies, procedures, or entitlements, often in the form of a chatbot. #### 3. Finance agents In finance, most common agentic AI use cases often relate to managing interactions with the wider organization, applying defined logic to process submissions such as expense claims or budget requests. For example, internal rules that expenses under a certain threshold can be automatically approved, allowing agents to autonomously process requests or escalate them to a human colleague. #### 4. Enterprise-wide assistants Many organizations are rolling out unified experiences for accessing employee services, enhancing end-user experiences and providing exceptional levels of centralization across internal services. This can take the form of an enterprise chatbot that accepts natural language triggers from end users, covering various internal policies, processes, or workflows. ### Customer-facing services Many organizations are rolling out agents for external-facing workflows too, applying principles to enhance customer satisfaction rates. #### 5. Customer service agents By providing unified experiences for resolving customer queries, AI agents can enhance experiences for prospective and existing customers. They must interpret natural language queries to determine the users' underlying requirements and return the appropriate information or take actions, securely managing customer data. #### 6. Sales prospecting agents Sales can benefit from agentic AI by reducing the burden of manual tasks, such as qualifying leads with minimal data input, allowing agents to perform enrichment autonomously. ### Software development Some of the most prevalent agentic AI use cases are made by software developers. #### 7. Code generation agents These accept natural language inputs and output working code, often with access to existing codebases. They can generate initial outputs, check for completeness or accuracy, and make adjustments to improve efficiency. #### 8. QA and testing agents Agents can perform QA and testing tasks autonomously, identifying, designing, and executing tests while interpreting the outputs to make necessary adjustments to the code. ### Workflow automation Agentic AI use cases also mimic core actions taken in traditional workflow automation tools, building efficiency within day-to-day workflows. #### 9. Document processing Agentic AI can interpret documents and extract key values without relying on standalone OCR solutions, providing greater flexibility and efficiency. #### 10. Integrating SaaS tools Agents can autonomously select, populate, and trigger requests across SaaS platforms, increasing the development burden required to output solutions and reducing impacts from individual API changes. ## Turn data into action with Budibase Budibase is the all-in-one open-source AI workflow toolkit that connects to any data, API, or LLM. We offer extensive support for a huge range of LLMs, RDBMSs, NoSQL tools, APIs, and more, alongside AI agents, autogenerated UIs, and visual automation builders.