The intelligent technology at the heart of the AI agent is the large language model (LLM). A powerful class of machine learning (ML) systems designed to process and generate natural language, LLMs allow AI agents to understand goals, break them into tasks, and communicate their solutions effectively. However, LLMs alone are not enough for AI agents to fully execute complex actions. AI agents can utilize “tool calling” to extend their capabilities by using external tools such as APIs, databases, or even other AI models, to gather real-time information, analyze data, and adapt their workflows.
To do this, these agents follow a specific sequence of stages:
- Goal definition and task planning
The process begins with the user providing the AI agent with a specific goal or objective. Once the goal is set, the AI agent initiates planning by breaking down the objective into smaller, actionable tasks. For more complex goals, the AI agent maps out an entire sequence of subtasks, creating a complete roadmap to help direct its actions in future stages. - Data gathering and knowledge acquisition
To carry out the tasks and subtasks identified in the previous stage, AI agents need access to relevant information. They gather data from various sources (internet, internal databases, external tools, etc.). In cases where the AI agent lacks specific knowledge, it can use APIs or connect with other systems via MCP to help fill in the gaps. - Decision-making and execution
Once equipped with the necessary data, the AI agent employs LLMs to make decisions. It evaluates the information, determines a possible course of action, and begins executing the tasks. - Monitoring and feedback integration
As the AI agent progresses through its tasks, it continuously monitors the results of its actions, gathering feedback from both its environment and users. This feedback is essential for self-assessment and governance, as it allows the AI agent to adjust its approach if needed. The AI agent can also create new subtasks in response to feedback it receives, ensuring that it stays aligned with the user's ultimate goal. - Learning and improvement
After completing a task, the AI agent stores the data and lessons learned in its knowledge base. This allows it to refine its strategies for future interactions. Over time, this process makes it possible for the AI agent to become more accurate and efficient.
An efficient memory system is critical to the performance of AI agents, allowing them to store, retrieve, and update information as changes occur. AI agents ‘remember’ past interactions, decisions, solutions, and learned data, promoting coherence and relevance when performing tasks. Without a comprehensive memory infrastructure, AI agents may struggle with consistency, repeat past errors, or simply lose track of user preferences.
Effective memory systems make it possible for AI agents to:
- Build context
Persistent context allows agents to adapt to changing inputs, refine their outputs, and maintain alignment with user goals throughout longer workflows. - Coordinate more effectively
In environments involving multiple AI agents, each individual agent can access relevant information while maintaining its own distinct role.
A well-designed memory system gives AI agents access to relevant context from previous interactions and helps maintain continuity across individual and multi-agent workflows.
AI agents are complex systems made up of various components—some are inherent to all AI agents, while others are specific to the tasks only some agents are designed to accomplish.
Universal components provide the agent’s core functions. All AI agents include the following components, regardless of where or how they operate:
- AI agent architecture
The architecture is the AI agent’s foundation. This can be a physical structure, such as a robot with motors and sensors, or a software-based platform that relies on APIs and databases to provide essential support. The architecture houses all the tools and systems the AI agent needs to function autonomously. - AI agent function
The AI agent function determines how information collected by the agent is processed and transformed into actions. It is designed to map the incoming data to a set of responses or actions that match the agent’s objectives. - AI agent program
The AI agent program integrates the architecture and agent function into a working system of implementation. This includes everything from coding the logic behind the AI agent's decision-making to deploying it in the required environment. The AI agent program aligns the AI agent’s goals with the technical requirements needed for it to operate smoothly.
Depending on the tasks AI agents are assigned and where they’re designed to function, additional components may be necessary to enable more specialized capabilities. These conditional components are found only in specific types of AI agents:
- Sensors
Sensors allow the AI agent to gather data from its environment. For physical agents, this could refer to cameras, microphones, radar, etc., while software-based agents may use tools like web crawlers or file readers. - Processors
The processor is part of the AI agent’s ‘brain,’ responsible for handling the data from sensors and converting it into actionable insights. Processors perform the complex computations needed to analyze information and decide the best course of action. - Actuators
Physical actuators include robotic arms or wheels for movement (giving the AI agent the ability to move within the physical world), while digital AI agents may use tools to create files or send commands within software systems. These components carry out the actions dictated by the AI agent’s decision-making process. - Control systems
Control systems manage how the AI agent processes data and decides on actions. They coordinate between the sensors, processors, and actuators to ensure the AI agent operates as intended. In more advanced AI systems, control systems allow the AI agent to adapt and self-correct.
As previously stated, AI agents can take on different forms depending on the complexity of their goals and the environments in which they operate. The capabilities of AI agents range from simple, reactive functions to highly autonomous decision-making processes, and some AI agents may incorporate multiple different types and capabilities. These categories are not mutually exclusive; many enterprise AI agents combine characteristics of several types. Below are the primary categories of AI agents commonly used in different industries:
- Simple reflex AI agents
These AI agents operate within predefined rules and respond to specific stimuli. They are the most basic type of AI agent, functioning without memory or more complex decision-making. Simple reflex AI agents are suitable for straightforward tasks that do not require context or learning. These agents are most commonly used in manufacturing, facilities management, and other environments where processes are predictable and rule-based. - Model-based AI reflex agents
Model-based AI reflex agents are more advanced than their simple reflex counterparts. They maintain an internal model of the environment, which allows them to make more informed decisions. These AI agents use both current data and memory of past interactions to adjust their behavior. Industries such as logistics and robotics benefit from this approach, where systems must account for changing conditions while maintaining efficiency. - Goal-based AI agents
Goal-based AI agents are designed to achieve specific objectives by generating and executing action plans. These AI agents consider multiple potential actions and choose the most effective path to reach their goal. Healthcare and financial service organizations often rely on goal-based agents to manage complex workflows that require planning and prioritization. - Utility-based AI agents
Utility-based AI agents take decision-making a step further by evaluating different possible actions based on a utility function. This function measures the potential success of each action against criteria like efficiency, cost, or speed, making it an ideal approach for tasks where multiple outcomes are possible. These agents are especially valuable in supply chain management, transportation, and energy, where trade-offs between cost, speed, and efficiency must be continuously evaluated. - Learning AI agents
Learning AI agents adapt by learning from their environment and experiences. They can improve their performance by storing past actions and feedback, which they use to refine future decisions. They are widely used in customer experience, marketing, and e-commerce, where personalization and continuous improvement are key to performance. - Hierarchical AI agents
Hierarchical AI agents work as a coordinated group, with higher-level agents breaking down complex tasks into smaller, manageable ones. These smaller tasks are delegated to lower-level agents, which operate independently but report their progress back to the higher-level agent. This structure is useful for large, multi-step projects where different agents handle specialized subtasks. Large enterprises in sectors such as telecommunications, finance, and global supply chain benefit from this structure when managing complex processes across teams. - Copilots
Copilots are designed to assist humans by providing recommendations or completing tasks in response to human input. While not fully autonomous, copilots offer real-time support, augmenting human decision-making with AI-driven suggestions. These are most commonly used in knowledge work environments such as software development, marketing, and professional services. - Autonomous AI agents
Autonomous AI agents are fully self-sufficient systems capable of carrying out complex tasks without any human intervention. Unlike copilots, these AI agents can make decisions, gather data, and execute actions independently. They are most valuable in enterprise environments where continuous, real-time decision-making is required across workflows, such as IT operations, customer service, and business process automation.
As AI adoption accelerates, organizations are faced with a growing list of tools that seem similar on the surface but serve very different purposes. The key difference is execution. Chatbots, AI assistants, and custom GPTs can answer questions or recommend next steps, but AI agents operating inside enterprise workflows can carry work through to completion - not just recommending, but getting work done.
Regardless of the various types of AI agents, the benefits they deliver are generally consistent. In a recent PwC survey of companies adopting AI agents, 66% of respondents reported increased productivity, 57% reported cost savings, 55% reported faster decision-making, and 54% reported improved customer experience.
- Improved productivity
AI agents take over repetitive, time-consuming tasks, freeing up human employees to focus more of their time on higher-value work. - Enhanced efficiency
AI agents can handle multiple tasks or interactions simultaneously, processing and acting on enormous amounts of data in very little time. - Higher- quality outputs
They can integrate knowledge from various sources, collaborate with human users and other AI agents, and learn from their interactions to continually improve their output. - Reduced costs
This reduction in inefficiencies saves time while cutting overhead costs. - More -informed decision-making
AI agents use LLMs and data analysis to process real-time data, empowering organizations to make faster decisions built on reliable insights. - Reliable consistency
AI agents provide consistent, accurate outputs, eliminating the variability that comes with human involvement. - Increased scalability
AI agents boost scalability by adjusting to increased workloads without sacrificing performance. - Optimal customer experience
AI agents enhance the customer experience by offering personalized, prompt, and reliable service.
AI agents are already being deployed across the full range of industries to automate workflows and reduce operational overhead. In fact, in a McKinsey & Company 2025 global AI survey of 1,993 respondents in 105 countries, 62% of respondents said their organizations were at least experimenting with AI agents.
ServiceNow customers are already putting AI agents to work: Booking.com's AI agents get it right 97% of the time, and Bell Canada uses AI Agents to handle escalations. The following examples highlight how organizations are applying AI agents across industries, departments, and disciplines:
Financial institutions have traditionally relied heavily on manual reviews for compliance, risk analysis, and customer service. With AI agents, these processes become continuous and automated. Agents can monitor transactions, flag anomalies, and support decision-making for underwriting or fraud detection. This shift reduces processing time, improves accuracy, and allows teams to focus on higher-value financial strategies.
Instruction is often standardized, and that limits the ability to personalize lessons to better fit individuals. AI agents enable adaptive learning by tailoring content to specific student needs, tracking progress, and providing real-time feedback. This leads to more engaging learning experiences and improved outcomes by aligning instruction with each learner’s pace and style.
Healthcare systems often face administrative bottlenecks that reduce the time available to devote to patient care. AI agents can automate tasks such as scheduling, billing, and patient monitoring while also supporting diagnostics through intelligent data analysis. This improves operational efficiency and allows providers to focus more on direct patient interaction and treatment quality.
Customer service teams typically manage high volumes of inquiries, leading to delays and inconsistent experiences. AI agents improve this approach by delivering real-time, personalized responses, taking intelligent initiative to identify issues, and even executing resolutions (such as issuing refunds or creating support tickets). This results in faster response times, improved customer satisfaction, and reduced workload for human agents.
HR teams frequently have to manage repetitive processes, such as recruiting, onboarding, and employee support. AI agents streamline these workflows by automating resume screening, scheduling interviews, and handling employee requests. This reduces administrative burden and enables HR professionals to focus on strategic initiatives like talent development.
IT teams spend significant time managing incidents, monitoring systems, and troubleshooting issues. AI agents can autonomously detect anomalies and resolve common problems, improving system performance in the process. This helps reduce downtime and allows IT teams to focus more on innovation instead of routine maintenance.
Content workflows often require significant manual effort for drafting, editing, and distribution. AI agents can autonomously generate content, tailor messaging to specific audiences, and manage publishing schedules. This enables organizations to scale content production while maintaining consistency, reducing turnaround times, and lowering resource requirements.
As AI agents become more autonomous and integrated into critical business processes, organizations must establish clear frameworks for data privacy, security, risk management, and governance. Effective implementation requires organizations to control what agents can access and act on, establish appropriate human oversight, monitor agent behavior, and protect sensitive data throughout the AI agent lifecycle.
Despite growing adoption, governance is still developing: ServiceNow’s 2026 Enterprise AI Maturity Index, a survey of 4,500 executives across 19 countries and 12 industries, found that while 59% of organizations are using agentic AI, only 26% have systems to manage governance and compliance. The following steps can help organizations introduce AI agents while addressing security and operational requirements:
1. Set clear goals
The foundation of any AI agent implementation begins with well-defined objectives. Defining these goals from the outset creates a framework for evaluating success while ensuring that all AI solutions are tailored to meet business needs. Setting vague or overly broad objectives is a common pitfall, and can lead to unclear outcomes. To avoid this, organizations should define specific, measurable goals tied directly to business impact.
2. Evaluate and organize the data
Data is the backbone of AI agents, and the quality of the data defines their performance. Before deploying AI systems, organizations must audit existing data sources to ensure completeness, relevance, and accuracy. This includes cleansing the data of inconsistencies, redundancies, and inaccuracies that could lead to flawed outputs. One of the most common challenges is fragmented or inconsistent data across systems.
3. Select the appropriate AI agent
Not every agent is the right fit for every use case; the appropriateness of the AI agent depends on the complexity of the tasks it will perform. For example, a simple reflex agent may be enough for automating routine operations like scheduling, while more advanced learning agents will likely be needed for customer service or supply chain optimization. Consider factors such as decision-making autonomy, adaptability, and learning capability, and select an AI agent that is equipped to handle the specific challenges of the operational environment.
4. Integrate with current systems
AI agents perform best when deeply integrated with existing infrastructure (including tools and applications). Proper integration ensures that AI agents have access to real-time data, enabling them to operate effectively and in coordination with other business processes. Collaborate with IT teams to ensure that integration is secure and scalable. A key risk is failing to connect AI agents to the external tools and platforms they need to execute full workflows; without these integrations, agents may generate insights but be unable to take meaningful action.
5. Define access controls and governance policies
Clearly define what each AI agent can access, modify, and act on before deployment. Establish governance policies for data usage, decision-making, and compliance, and make sure agent permissions reflect the scope of each agent’s responsibilities. These controls can reduce the risk of unintended actions while creating clearer accountability across AI systems.
6. Prioritize the user experience
Whether for employees or customers, user experience should be central to the AI agent design process. AI agents should undergo usability testing to identify glitches, bottlenecks, or interactions that could frustrate users or disrupt workflows. For customer-facing agents, delivering personalized and timely responses is among the most important considerations, while internal agents will need to integrate easily into existing workflows. Early and frequent testing helps ensure the solution meets user expectations.
7. Continuously monitor, test, and improve
Once deployed, AI agents benefit from continuous monitoring and optimization as they adapt to changing needs and maintain high performance. By tracking key performance indicators (KPIs), organizations can assess the effectiveness of their AI systems and identify any areas that might need improvement. Organizations should also monitor agent behavior for anomalies and conduct regular performance testing to verify that agents continue to operate as expected.
8. Safeguard data privacy and security
Prioritize compliance with relevant data-protection regulations. Implement security measures such as strong encryption and appropriate protections for sensitive data, and regularly reassess security requirements as AI agents and their workflows evolve. Security testing and audits can help identify vulnerabilities and confirm that deployed agents comply with established security requirements.
AI agents are transforming how businesses achieve productivity at scale, and the ServiceNow AI Platform provides the resources for maximizing their potential. With ServiceNow AI Agents, organizations can apply AI agents across use cases in IT, customer service, HR, procurement, software development, and other enterprise functions. ServiceNow AI Agents support capabilities such as:
- Custom AI agents and skills tailored to specific business needs
- Integration with enterprise data, workflows, and systems
- Built-in governance and analytics to help keep agent activity aligned with business parameters
- End-to-end workflow execution and coordination across functions
- Text-to-action capabilities that allow users to initiate workflows through natural language commands
- AI Agent Advisor, which mines your data to identify the highest-impact agents for your organization and gets them live in minutes
Together, these capabilities help organizations move beyond isolated automation efforts and apply AI agents across connected enterprise workflows. For example, the City of Raleigh's AI Agent, Ral-E, now routes 98% of tickets correctly on the first try, returning more than 1,300 hours to IT staff each year and reducing IT service desk costs by 66%.
Demo ServiceNow today, and let powerful AI agents automate your path to success.