AI Agents for Business Operations + Use Cases and Tools
Agentic AI is quickly transforming the way businesses handle all kinds of internal processes.
Compared to traditional automation tools, AI agents are capable of handling a far wider variety of tasks, including those that might otherwise require human reasoning and decision-making. As such, the potential to improve efficiency, accuracy, and cost-effectiveness in our workflows is huge.
Today, we’re examining one of the most prominent clusters of use cases by diving deep into AI agents for business operations, including everything from key use cases to the tooling we can rely on to implement agentic systems.
Specifically, we’ll be covering:
- What are AI agents?
- Why are businesses turning to agentic AI?
- Business operations use cases for AI agents
- Solutions and tooling for implementing AI agents in business operations
Let’s start with the basics.
What are AI agents?
AI agents are a type of LLM-powered software system that is capable of independently reasoning, assessing context, and taking action in order to achieve a particular goal.
In other words, we can think of agents as AI systems that we can give an instruction to, which will then determine which steps need to be taken, before triggering relevant actions using connected tools.
This can work in a few different ways. Some agentic systems continuously monitor for events that require a response. Others can be triggered by defined actions - for instance, as part of a wider automation rule.
Many AI agents provide natural language interactions for end-users. That is, we can communicate with them using an AI chat interface .
This means that users can describe the issue they’re facing in their own words. The agent can then follow up to request additional information or propose a solution for the users to give confirmation to before it takes action.
One of the key characteristics of an AI agent compared to other automation tools that we might use for business operations is the ability to utilize tools, data, and other resources as and when they are required.
This means that agents can operate autonomously within the confines of the tools and resources that we make available to them, based on independent reasoning. This enables agents to automate a wide range of tasks that might otherwise require human input.
How does agentic AI work?
Before we start thinking in more detail about business operations use cases for agentic AI, it’s helpful to have a broad understanding of how agentic systems actually work.
As with any AI-powered system, this centers around a large language model (LLM). This is a piece of software that has been trained on large amounts of data to be able to interpret, predict, and generate text.
This is what enables the agent to accept and analyze incoming instructions, in order to determine the user’s goal and which steps are required to reach it.
In addition to this, AI agents rely on a range of architectural tools for reasoning and decision-making. This includes memory stores for handling context, as well as orchestration tools and runners to manage tasks.
As we hinted at a few moments ago, agents also rely heavily on integrations with external tools, systems, and data sources. This can be handled in a few distinct ways, including API connections and MCP servers.
The key thing is that these are provided with clear instructions on how and when specific functions are to be used within business workflows, in order for the agent to effectively determine which tools can be invoked within individual executions.
To ensure oversight and control, agentic systems should also provide capabilities around observability, monitoring, tracing, and access control.
Lastly, we’ll need an interface layer in order to interact with AI agents. For end users, this typically takes the form of a chat UI, enabling them to communicate with the LLM via natural language.
To see some of our options here, take a look at our round-up of Open WebUI alternatives .
Why are businesses turning to agentic AI?
Next, we can begin to think in more practical terms about implementing AI agents in our own business operations.
To do this, we need to have a clear understanding of the potential benefits that we can achieve here, as well as some of the challenges that we’re likely to encounter when implementing agentic solutions.
Benefits
We’ll start with the benefits. We’ve already touched on these at a high level, in the sense that we know agents drive efficiency, accuracy, and cost-effectiveness in our workflows by expanding the scope of the kinds of tasks that can be automated.
With a better understanding of how agents actually work, we can think about this in more detail.
As you might expect, the most important element to this is the potential to reduce administrative workloads. For example, within reasonably repetitive but high-volume tasks, such as ticket triage, L1 support, or simple approval workflows.
By providing an effective solution for automating these kinds of tasks, agentic AI has huge potential to provide savings in terms of labor hours.
Additionally, this frees our colleagues in internal operations teams to work on more complex requests and tasks, helping to enhance productivity and efficiency levels across teams more widely.
By cutting our reliance on manual admin work, we can also boost accuracy levels within our workflows, as well as reducing the scope for human error by implementing more tightly controllable business logic.
At the same time, agents provide a greatly enhanced UX for end users. On the one hand, by facilitating natural language interactions, we can hugely improve the accessibility of operations workflows, providing a single point of contact to interact with a variety of internal services.
On the other hand, as implementing agentic systems can help to speed up resolutions across our internal operations, users can expect their issues to be resolved much more quickly, helping to boost satisfaction levels.
Challenges
Of course, none of these potential benefits can be taken for granted. Rather, implementing AI agents within business operations is a large undertaking, with a huge range of challenges and other considerations we’ll need to account for to achieve ROI.
- Technical integration - agents need stable, secure connections into existing systems.
- Data quality and governance - messy or inconsistent data will directly degrade an agent’s decisions and outputs.
- Operational reliability - without guardrails and monitoring, agent errors can cascade through a workflow.
- Security and access control - agents should only be able to take actions on behalf of a user that are suitable for their level of access.
- Compliance and risk management - autonomous actions must be traceable and auditable to meet regulatory standards.
- Change management - introducing agents shifts responsibilities, requiring clear communication and role adjustments.
- Cost and time to production - integrating agents into real workflows often takes more engineering effort than expected.
- Ongoing maintenance - agents drift as systems or data change, so they need continual updates and oversight.
- Data sovereignty - organisations must control how and where data is stored and how it is consumed by LLMs.
- Model selection - model selection affects everything from latency and accuracy to controllability and total cost.
Notably, the relative importance of each of these, along with the practical steps we need to take to reflect them, could vary quite a bit from one team to the next.
For instance, security and data management issues are typically top priorities in larger organizations with more complex operations, while smaller teams might be more focused on choosing a solution that they can implement quickly and easily.
Business operations use cases for AI agents
Next, we can begin to think about some more concrete examples of business operations use cases for agentic AI.
To do this, it’s first helpful to consider the specific characteristics that make different workflows and processes suitable for handling with AI agents.
Generally speaking, these are tasks that follow clear rules, rely on information that already exists in internal systems, and occur frequently enough that manual handling slows things down.
However, these are typically not so repetitive that a traditional, deterministic automation could handle them effectively. That is, there should be some requirement for reasoning and context awareness.
Of course, these kinds of workflows exist across all kinds of operational areas.
Many of the most common examples fall under the umbrella of IT service management. This includes tasks such as interpreting incoming issues, collecting context, routing requests, and carrying out simple actions.
For example, handling password resets or ticket triage.
Another key set of workflows here involves handling requests for access to a given resource, as well as approval processes more broadly.
These kinds of workflows revolve around assessing who the user is and if they have provided information that complies with the business rules that are in place for granting access.
These are relatively straightforward processes, but they may involve an element of independent reasoning. For instance, to assess a user’s justification.
AI agents for business operations are also widely used in more complex workflows, particularly those that require some aspect of coordination or where individual executions don’t necessarily follow the same linear pathway.
One of the most prominent examples of this is employee onboarding. The specific actions required to onboard an employee can vary greatly according to their role, while carrying these out can often involve managing a complex set of dependencies.
Therefore, AI agents are uniquely suited to automating and overseeing these kinds of tasks. Take a look at our guide to managing employee onboarding processes to learn more.
Solutions and tooling for implementing AI agents in business operations
Lastly, it’s important to be aware of the kinds of solutions that are available on the market today to help us implement AI agents within our business operations.
As you might expect from such an in-demand class of software, several vendors have brought solutions to market in recent years, aimed at distinct organizations and target users.
At one end of the spectrum, we have platforms offering a high level of customization, which aim to empower developers to create highly bespoke AI agents, typically using custom code.
These normally take the form of AI agent frameworks , such as LangGraph or AutoGen. These provide an architecture and reusable elements that enable developers to create custom agents, connecting them to their own tech stack, without having to build all parts of this from scratch.
Or, we may combine these with platforms that offer off-the-shelf solutions for aspects of agentic systems. For example, a range of tools are available for shipping interfaces on top of agentic layers.
Naturally, these offer a huge degree of control and flexibility, with the caveat that we’ll need suitable technical skills to use them.
Alternatively, a range of platforms have also come to market offering more abstracted experiences for building agent solutions for business operations, including with low-code or visual development tools.
Prominent examples here include the likes of Zapier, n8n, and CoPilot Studio. These kinds of tools offer more flexibility than off-the-shelf agents, but don’t present the same skills barrier as more heavily code-based tools.
Take a look at our guide to low-code AI agent builders to learn more.
Lastly, a number of vendors also offer largely off-the-shelf solutions for implementing agents within business operations.
Often, these are offered as part of a wider solution for a particular vertical. For example, most prominent ITSM and HRIS platforms offer configurable AI agent tools for relevant processes and workflows.
Turn data into action with Budibase
Budibase is the open-source, low-code platform that empowers IT teams to turn data into action.
We offer extensive connectivity for external data, autogenerated UIs, powerful AI-driven automations, custom RBAC, optional self-hosting, and much more.
Check out our features overview to learn more.