AI EDUCATION: What Is Agentless AI?

Welcome to another AI Education—this week, we’re going to look at another side of a coin we flipped some months ago when we discussed AI agents. AI agents, as a refresher, are software that interact with a user to perform tasks. Generally speaking, both the agent and the tasks performed are powered by AI. So an agent will use natural language processing, large language models and generative AI to interact with a user in plain speech, whether written or spoken, while using AI to query a database to perform whatever actions the user requests. Most artificial intelligence applications we are familiar with involve agentic AI. 

Voice-powered assistants like Apple’s Siri and Amazon’s Alexa are examples of consumer-facing AI agents. Agents pre-date generative artificial intelligence, acting as extensions of a software’s user interface, like liaisons between the user and the software’s underlying functions, automatically authorizing some of a software’s functions on the user’s behalf. Traditionally, though, agents operate within predefined rules and use structured data, focusing on specific tasks 

Modern AI agents take the concept a step farther and are capable of learning about their user’s preferences over time and experience, and using that information to make decisions. But now, another form of AI that eschews the need for agents altogether has emerged. 

What Is Agentless AI? 

But not all software needs agents, and thus, not all AI needs to be agentic. Agents are resource intensive and can be costly to organizations. Thus, several applications for agentless AI have emerged. While the specifics of these applications differ greatly, they all share a few characteristics. They often do not need to be installed on systems and run directly, but are available via the cloud and APIs. 

Agentless AI accesses data via these APIs and through network data, logs and user information, without being installed on a network or an end device. Thus, it can be deployed faster, at lower cost and with less operational overhead than agentic AI, and it can be scaled more easily. 

AI That You Won’t Know You’re Using 

Moving forward, agentless AI will often be deployed in AI-as-a-service, event-driven settings, offering point solutions for businesses and users—and because you won’t have to install it, it will kind of just happen without you explicitly knowing that it is happening—an AI will be queried or deliver data on your behalf, or contribute to the completion or performance of some task and it will be like breathing, you won’t even think about the fac that it’s happening. 

That’s really because most agentless AI applications are being built around tasks that humans don’t necessarily want to intervene in. 

Let’s look at three existing applications for agentless AI. 

IT 

There are several IT-oriented applications for agentless AI, one is as a coding assistant, as it is not always necessary to employ agentic AI to fulfil a user’s coding needs. 

Another is as a systems operator—agentless AI can oversee network and cloud performance without the need to install an agentic operator. Instead of residing on every device to collect data directly, agentless AI accesses data from the network protocols themselves. This ability carries over into the industrial world, where agentless AI can be used to train and oversee networks of machines connected to an “internet of things” without having to be present on either the network or the machines themselves. 

Security 

Agentless AI is becoming a necessary component of modern cybersecurity regimes, but it’s a slow transition. In the past, most security software has been agentic, with installed endpoint agents surveilling systems. 

Agentless security, which resides on the cloud, uses API logs, transaction data and event-driven prompts to augment AI security agents, providing more complete protection for networks and data. An agent is capable of focusing on specific vulnerabilities and acting independently, while agentless security monitors all of a network’s data to find issues 

There are security benefits to using agentless AI, as well, as data can be managed or processed at a centralized location. 

Highly-Regulated Industries 

Because agentless AI can analyze information without accessing sensitive data kept by businesses in regulated industries like healthcare and finance, it’s particularly useful in these fields.  

In healthcare, agentless AI is used to provide analytics without running afoul of information privacy rules. So the operators of hospitals and clinics, as well as major health systems, can understand how people use and move through their services without accessing personally identifiable information or sensitive health data. 

Similarly, in finance, agentless AI can help monitor accounts for fraud and other suspicious activities without intruding on client privacy. 

A Metaphor but Not an Example: Contact Centers 

So, agentless contact centers don’t really refer to agentless AI itself, but the concept is similar and they are using AI to fully automate the customer service experience. Rather than interacting with an agent on the end of the line, artificial intelligence handles all inquiries without the need for human intervention. They don’t use agentless AI, instead, they’re using AI agents to replace human operators—instead of having a human understand the caller’s problem, seek an answer, and deliver that answer to the caller, an automated system performs the task without intermediation.