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Everything you need to know about AI Agents
AI is everywhere nowadays, from chatbots answering your questions to smart tools that help manage your calendar. But behind the scenes, something more powerful is starting to take shape: AI agents, that can actually take action, make decisions, and work alongside people to get things done. So what exactly are they, and how do they work?
7 minutes
Table of contents
What is an AI agent?
AI agents in ServiceNow’s Now Assist platform are intelligent software systems designed to work independently, making smart decisions based on data, automating processes, and optimizing workflows.
But what does that really mean in practice?
Every person has their own skills—some are great at analyzing data, while others excel at problem-solving or communication. AI agents work the same way. Each AI agent has specific skills, like summarizing incidents or analyzing data to determine the best team to handle an issue.
For example, one skill might be summarizing an incident, another might be analyzing the summary and assigning it to the right team, and yet another might be selecting the best person for the job. These individual skills, when combined, create a functional AI agent. Just like how humans with different abilities collaborate to complete a task.
How AI agents work?
AI agents and Large Language Model (LLMs) are related, but they are not the same thing. An LLM like OpenAI’s GPT-4 is an advanced AI model trained with huge amounts of data, which allows them to understand and generate human-like text, analyze data and answer questions. However, an AI agent is a system that takes actions autonomously based on data and decision-making algorithms that may use LLM as core component.
To make it easier, imagine an LLM is just a brain that processes language, but it doesn’t take actions by itself, while an AI agent is a full system that makes decisions and takes actions often using an LLM as part of its intelligence.
A large language model can summarize an incident report, but an AI agent goes further—it can summarize the report, assign it to the right team, and initiate follow-up actions. Additionally, it learns to adapt to customer expectations over time and uses memory to plan future actions based on past interactions.
Although AI agents operate autonomously when making decisions, they are not humans, thus they are significantly influenced by us. This is what we call “human in the loop” since we are still involved in the processes. The first layer of influence comes from the team that designs and trains the AI system. The second influence comes from the team responsible for installing the system and granting user access. Finally, the user plays a crucial role by assigning tasks or setting goals for the AI agent to accomplish.
The AI agent’s approach depends on the complexity of the task. For complex tasks, it develops a structured plan, breaking them into smaller steps and refining its performance over time. In contrast, for simpler tasks, the AI agent can optimize its performance using the available tools without the need for a predefined plan.
AI Agents in real-world use cases
Take ServiceNow as an example. Imagine a company experiencing an issue that requires an engineer’s immediate attention. Normally, they would rely on a designated engineer, but due to an unexpected delay, they need a replacement as soon as possible. To solve this, the user tasks an AI agent with finding a qualified engineer who can respond quickly.
The AI agent begins assessing the situation using data already available in ServiceNow, such as employee schedules, current availability, and skill sets. However, even with this rich context, it still can’t identify the best engineer for the job. To refine its decision, the agent consults additional insights, such as past performance or detailed expertise, which may be stored outside ServiceNow in specialized systems accessed through the Integration Hub and Workflow Data Fabric.
By synthesizing insights from multiple sources, the AI agent identifies the most suitable engineer and presents the best option to the user. This ability to integrate information from different tools and specialized systems is what makes AI agents more flexible and powerful than traditional AI models.
But what happens next?
After the AI agent selects a replacement engineer, it records what it has learned, including any user feedback to improve its performance for future requests. For instance, if the user emphasizes that response time is more important than expertise in emergencies, the agent will prioritize availability in similar situations.
If other AI agents were involved in the process, such as one that evaluates engineer expertise, their feedback can also be incorporated. This multi-agent feedback loop reduces the need for human oversight, allowing the system to make decisions autonomously. At the same time, users can still provide direct input throughout the AI’s reasoning process to ensure the results align with their needs.
What ServiceNow Offers
Normally, companies have to manually combine different skills to create an AI agent tailored to their needs. Now, ServiceNow is changing that. Instead of assembling separate AI components, businesses can simply “turn on” a pre-built AI agent that solves problems automatically, instead of having to build everything from scratch. We call these out-of-the-box skills.
Think of ServiceNow as a big box of Lego pieces. Some of these Lego pieces (skills) come standard, so you don’t have to assemble them from scratch. But instead of just individual pieces, you can also get pre-assembled structures (AI agents). In case you need something more customized, you have the flexibility to add special pieces or build your own solutions to personalize your Lego more.
AI Verticals and Business Domains
A major trend in AI is the focus on verticals — solutions designed for specific industries rather than general business functions.
- Horizontals are common business functions like HR or finance, which exist in every company regardless of the industry. Every company needs payroll management, employee onboarding, and financial reporting. These functions remain consistent whether you’re in retail, healthcare, or manufacturing.
- Verticals are industry specific. Take manufacturing as an example: an AI agent might monitor supply chains to ensure some components, like car batteries, arrive on time. If a delay occurs, the AI can automatically find solutions, such as rerouting shipments or adjusting production schedules.
An AI agent specialized in manufacturing wouldn’t be useful in HR, just like ordering a beer doesn’t involve an HR service. That’s why vertical AI solutions are gaining traction, because they’re built for specific business domains.
Benefits of AI agents
- Increased efficiency: AI agents take care of repetitive, time-consuming tasks so employees can focus on more important work. They streamline workflows and reduce human errors. That’s why organizations rely on them to improve productivity and achieve their goals faster.
- Smarter decision-making: by analyzing large amounts of data, AI agents provide insights and recommendations, helping businesses make better decisions. They continuously learn and improve, making responses better over time.
- Cost savings: AI agents help businesses cut costs by automating tasks and avoiding human errors or manual tasks. Unlike human teams that require more staff to handle growth, AI helps your business scale effortlessly.
- Personalized & Improved User Experience: AI agents adapt to user preferences, making interactions feel more natural. They deliver personalized recommendations no matter the field they are working on, whether in customer service, marketing, or internal operations.
- Scalability without limits: one of the biggest advantages of AI agents is their ability to handle virtually unlimited amounts of data. While human memory and attention are finite, AI agents retain and utilize all relevant information to drive smarter.
From Basic AI agents to more agentic AI
Currently, AI agents handle simple, structured tasks. They don’t replace humans but assist them. However, as AI evolves, these agents will take on more complex roles, making independent decisions.
This is where companies often hesitate. Imagine AI approving budgets without human oversight. Who makes the final call? That’s the challenge of moving toward truly Agentic AI, where the system autonomously makes decisions without waiting for human approval.
As AI agents become more advanced, their ability to operate independently will continue to grow. And with projected intelligence levels that may surpass human IQ in the upcoming years, the question is no longer whether AI can perform tasks, but how much autonomy companies are willing to give.
Are we ready for AI that makes decisions without human approval? That’s the big question companies will need to answer as AI technology continues to advance.