<- All posts

What are Enterprise AI Agents?

Ronan McQuillan
10 min read · Jul 18, 2025

Interest in agentic AI has exploded over the past few years. In particular, businesses of all kinds are adopting AI agents to build efficiency, reliability, and scalability into their internal processes and workflows.

However, as with all kinds of software tools, what an effective solution looks like can vary from one organization to the next.

Indeed, a range of platforms have come to market in recent years for building and managing AI agents, but these often target very different segments and user personas.

Most importantly, the key considerations, pain points, and decision factors will be very different within small and medium organizations compared to large enterprises.

Today, we’re exploring one corner of the important subcategory here by diving deep into enterprise AI agents, including what these are, how they differ from other agents, and the specific issues we’ll need to be aware of when deploying solutions of our own.

Specifically, we’ll be covering:

Let’s jump right in.

Enterprise AI Agents

What is an AI agent?

AI agents are software systems that utilize LLMs to enable them to autonomously reason and act within an environment.

These are also connected to relevant tools and other resources, meaning that agents are able to trigger specific actions. Agents also utilize memory stores and other tools to allow them to learn from executions, and continuously refine and improve their performance.

Ultimately, the goal is to mimic human interactions within workflows. This means that agents can receive a high-level goal, assess the context of the request, determine how to proceed, and then take action.

Agents can work on their own, in conjunction with human users, or alongside other agents, in a multi-agent system.

AI agents are used across a diverse range of use cases, including handling user-facing and background processes.

What makes an agent enterprise-grade?

With a high-level understanding of what AI agents are and what they do, we can move on to considering how we can differentiate between enterprise agents and any other agentic system.

That is, how do AI agents need to differ in order to be effective in an enterprise context?

It’s helpful to think about this at a couple of distinct levels - the specific challenges that enterprises face, which smaller organizations don’t, and the capabilities and features enterprises will prioritize to meet these challenges.

Let’s check each out in turn.

Usage

First of all, we can think about what enterprise usage of AI agents looks like, including the specific ways that enterprise user bases, use cases, and IT environments differ from their SME counterparts.

Naturally, one factor here is scale. The most obvious element to this is that enterprise user bases for any given process or tool are typically considerably larger.

This has a number of important implications, including making it a greater challenge to administer and provision access and authentication.

On top of this, AI agents in an enterprise context often have a comparatively wide scope, spanning a diverse range of processes and functions.

In other words, enterprise AI agents differ from other agentic systems in that they generally need to interact with a much larger set of resources, tools, and processes, whether within a single functional area or in an enterprise-wide context.

We’ll come to use cases a little later, but one high-level example of this is an enterprise chatbot , which enables users to access all internal services across the organization through a natural language interface.

Managing this effectively can be a highly complex task, as different users across the organization will have their own roles and permissions across individual processes, including relating to which data or actions they can access.

We’ll discuss some of the ways we can manage this in the following section.

Lastly, large enterprises typically have heightened security and compliance requirements more generally. For example, in terms of handling internal users and managing external threats or malicious actors.

For some of the specific issues involved here, you might like our guide to AI agent security .

Capabilities

With a broad understanding of the specific ways that enterprise systems differ from other kinds of AI agents in terms of usage, we can begin to think about the more granular capabilities that we’ll need to priortize to achieve this.

One of the key things we discussed is authentication and authorization. Many enterprises have strict requirements around using specific SSO tools, including particular OIDC or SAML solutions to authenticate users.

So, whichever AI agent solution we opt for will need to support the relevant providers we need.

On top of this, enterprises require extensive flexibility when it comes to integration capabilities. That is, agents need to be capable of integrating with a range of tools, including ERP platforms, CRMs, ITSM suites, SaaS tools, legacy apps, data sources, and more.

More importantly, we need to integrate agents with existing tools in a secure manner. One element of this is the security of the connection itself, including utilzing encryption both in-transit and at-rest.

Additionally, we’ll need to implement access control policies and tools that are appropriate for the needs of end users within workflows and processes. In particular, many enterprises opt for time-limited access and least-privilege principles within their agentic systems.

For security reasons, many enterprises will often prioritize self-hosting.

Lastly, we’ll need to consider additional capabilities that make agentic AI viable in an enterprise context, including advanced observability, traceability, and auditing tools.

Benefits of enterprise AI agents

Having seen some of the key ways that enterprise AI agents differ from their SME equivalents, both in terms of usage and functionality, it’s worth thinking about why so many large organizations are turning to agentic AI.

Some of the most important high-level benefits of agentic AI for enterprises include:

  • Automating complex tasks - Agents can be used to handle tasks that require contextual reasoning, and would therefore be difficult to automate with deterministic solutions.
  • Operational efficiency - By replicating human actions, AI agents free up our service colleagues to work on higher priority tasks.
  • Enhanced decision-making - Providing real-time, data-driven insights that empower businesses to make better decisions.
  • Improved resolution times - Speeding up resolution times for requests and issues.
  • Unified experiences - Furnishing end users with a single interface to handle all kinds of internal services.
  • Increased productivity - Reducing the labor hours required to complete tasks.
  • Scalability and flexibility - Providing easier scaling than human-led workflows.
  • Risk mitigation - Utilizing agents to monitor and identify potential risks, and potentially even put mitigation measures in place.
  • Availability - Providing 24/7 access to services.

In short, AI agents are transformative for large enterprises, enabling us to automate a much wider range of tasks than would be possible with traditional automation tools.

In turn, this positions enterprise agents as a powerful solution for building efficiency in a huge range of processes and workflows across the organization.

Key use cases

To flesh out our understanding of what this could look like in the real world, we can move on to thinking about how enterprise AI agents are commonly used.

As we hinted at earlier, this is quite a broad question, in the sense that part of the appeal of AI agents is their ability to be deployed to span multiple departments and functions across the enterprise.

At the same time, there are a few important key trends within this that we ought to be aware of.

Possibly the most prevalent use cases for AI agents in large enterprises are within internal service functions, especially HR and ITSM.

Here, the value-add is providing end users across the organization with a singular, unified experience for accessing defined services. For instance, lodging an IT support ticket, booking a facility, or scheduling time off.

The goal is to remove the need for human colleagues within service departments to process routine tasks. Instead, the agent determines what it is the end user is hoping to achieve, and triggers the relevant workflow to action this.

In a similar vein, many large enterprises also deploy AI agents in customer-facing workflows. For instance, using chatbot agents to handle and process incoming after-sales or customer support enquiries.

In addition to unified, enterprise-wide service management, many organizations utilize AI agents for more specific, discrete tasks.

Common examples of this include an anomaly detection or incident monitoring, where an agent is configured to identify when an issue has occurred, along with how it should be handled, including escalating to human teams.

To learn more, take a look at our guide to AI agent use cases .

Challenges

While there appears to be no slowing down the pace of AI agent adoption in large enterprises, it’s nonetheless important to have a realistic view of their potential downsides, limitations, and challenges, which may lead us to decide that an alternative solution is more suitable for our needs.

Some of the key issues we’ll need to be cognizant of here include:

  • Upfront cost - Agentic AI might often be more expensive to implement than traditional automation solutions.
  • Integration challenges - difficulties relating to integrating agents with the necessary tools and resources, especially legacy systems.
  • Data quality issues - AI agents are reliant on relevant data that must be of a suitable quality.
  • Security considerations - including around data exposure, access control, authentication, and other considerations, as we discussed earlier.
  • Internal resistance to change - many enterprises encounter resistance to change, both from high-level decision makers and on-the-ground users.
  • Agentic sprawl - an increasing number of enterprises are reporting issues relating to managing multiple, separate AI agent platforms, often in an uncoordinated manner across different teams.
  • Monitoring performance and outcomes - including understanding what agents are doing and why, along with the real-world business value they’re delivering.

Of course, none of these are necessarily deal-breakers in and of themselves. Rather, what they highlight is the need to have a realistic picture of when agentic systems are the most appropriate solution, and when we might want to consider alternative options.

Check out our guide to AI agentic workflows .

Tooling and resources

Lastly, it’s worth considering enterprise AI agents from a more technical point of view.

More specifically, it’s important to understand the tools and components that we’ll need in order to develop, deploy, and manage AI agents.

There are a few different potential configurations of this.

Many solutions are built utilizing what are known as AI agent frameworks. These are largely code-based tools that provide reusable elements and components for key development tasks, such as dealing with memory, prompt-chaining, tool-calling, and more.

The goal here is to expedite the process of building, deploying, and managing AI agents while retaining the flexibility of code.

To learn more about this, take a look at our guide to the top AI agent frameworks .

Alternatively, more and more teams are turning to visual development solutions to build AI agents, including AI features within existing platforms and dedicated agent builder tools.

These can vary in terms of the technical skills that are required to output solutions, from fully no-code offerings to low-code options.

On top of this, they may also vary in terms of their enterprise-readiness, including capabilities for tracing, observability, auditing, and other tools large organizations are likely to prioritize.

To learn more, take a look at our guide to the top no/low-code AI agent builders .

Turn data into action

Budibase is the open-source, low-code platform that empowers IT teams to turn data into action.

With extensive external data support, autogenerated UIs, powerful automations, powerful AI capabilities, custom RBAC, free SSO, and optional self-hosting, there’s never been a better way to ship professional, secure internal tools.

Take a look at our features overview to learn more.