Agentic AI sounds as though it’s synonymous with ‘AI agents,’ but that’s not actually the case. Yes, both involve artificial intelligence performing tasks independently, but they differ in how they operate and the levels of autonomy they possess:

• Agentic AI 

Agentic AI operates with a higher degree of self-determination, continuously analyzing information, adjusting its strategies, and making decisions without waiting for human input. It can identify objectives, break them down into tasks, and refine its approach based on new data. Unlike traditional AI systems, it is not confined to a fixed set of instructions; it adapts dynamically to changing conditions.

• AI agents 

AI agents are designed for specific tasks and function within well-defined parameters. While they can still gather data, process information, and execute actions, they rely on preset rules or external commands to operate effectively. Many AI agents incorporate machine learning (ML) capabilities, but their decision-making is limited to predefined objectives. They do not proactively change their goals or redefine their methods unless instructed to.

In other words, AI agents function within defined constraints; agentic AI takes a broader, more adaptive approach. 

Similarly to agentic AI and AI agents, there are those who group AI agents and chatbots together as a single technology. In this case, their differences can be summarized as follows:

• AI agents 

AI agents can handle more complex interactions than chatbots, often integrating with external systems to perform specific functions. However, as previously stated, they do not independently redefine their goals or adapt beyond their pre-established learning models.

• Chatbots 

Chatbots are built primarily for conversation, using predefined scripts or ML to generate responses. They excel at answering questions, guiding users through workflows, and automating basic interactions, but while some advanced AI chatbots can recognize context and improve their responses over time, they do not typically analyze external data or take action beyond conversational tasks.

Finally, it is worth comparing Agentic AI and generative AI (GenAI). GenAI focuses on generating content while agentic AI is designed to act and make decisions autonomously:

• Agentic AI

While agentic AI may leverage generative AI models as part of its decision-making process, its primary function is to act and adapt, not to generate content.

• Generative AI

GenAI specializes in producing outputs (such as text, images, audio, and code) based on patterns learned from large datasets. It responds to user prompts but does not set its own goals or take independent action. While some models can refine their responses over time, generative AI lacks the ability to autonomously plan, reason, or make decisions outside of its designated task of content creation.

Defining agentic AI in contrast to other, similar technologies is only the beginning. To more fully understand what it is capable of and how it can best be applied to solve enterprise-level issues, it is worth taking a look at Agentic AI’s defining attributes. These features allow AI to operate independently while maintaining accountability: 

• Decision-making  

Agentic AI processes copious amounts of information to determine the best course of action—without needing constant human input. It weighs available data and takes into account multiple factors, and then selects an appropriate response based on the goals it has been provided. 

• Problem-solving  

Agentic AI takes a structured, probabilistic approach to solving complex challenges. It perceives its environment, reasons through available options, executes actions, and refines its approach through continuous learning. 

• Autonomy  

An essential characteristic of agentic AI is its ability to operate with minimal supervision. It does not require step-by-step instructions to complete tasks but instead follows overarching objectives. 

• Interactivity and control  

Autonomy does not mean operating in isolation—agentic AI interacts with humans, systems, and other AI components to refine its actions. It can gather user feedback and alter its approach based on human oversight. Similarly, built-in safeguards keep the AI ethically and organizationally on course.

• Planning  

Agentic AI models can manage multi-step workflows by breaking down objectives into smaller structured tasks. They evaluate dependencies, anticipate potential obstacles, and adjust execution strategies as conditions change.

• Data privacy  

Because agentic AI interacts with sensitive information, it must incorporate strict cyber-security and data-privacy measures. This includes encrypting stored and transmitted data, applying access controls, ensuring compliance with industry regulations and company policies, etc. 

• Monitoring for malicious behavior  

Agentic AI must be continuously monitored for unintended or harmful actions. This includes detecting potential biases in decision-making and identifying anomalies that might indicate a data breach. 

• Audit logging and traceability  

To ensure accountability, agentic AI should maintain detailed logs of data sources and the actions it has taken. These logs allow organizations to trace how conclusions were reached, making it easier to investigate and refine the AI’s behavior. 

• Performance monitoring  

Like any enterprise system, agentic AI requires ongoing performance evaluation. Organizations must track key metrics and business-relevant KPIs. Keeping a close eye on performance helps inform any adjustments that might be made as the AI model matures.

Like we said, automation isn’t new—agentic AI just takes it in a new direction, changing what is possible. And with its ability to act more independently than what has traditionally been possible, it delivers a number of clear business advantages: 

• Increased efficiency and productivity 

By handling time-intensive processes with minimal intervention, agentic AI opens up opportunities for human employees to invest more of their time and efforts into higher-value activities. 

• Enhanced customer and employee experiences 

With its ability to interpret context and adapt responses, agentic AI delivers more personalized interactions. Customers receive faster, more relevant support, while employees benefit from agentic assistance to support their decision-making and optimize their workflows. 

• Strategic human-AI collaboration and empowerment  

Rather than replacing human roles, agentic AI serves as an intelligent partner that integrates into existing processes. It intelligently assists with research, recommends solutions, and powers workflow optimization. Used correctly, agentic AI becomes a reliable tool for use throughout the workforce. 

• Greater specialization 

Traditional automation applies broad rules to a variety of tasks, but agentic AI makes hyper-specialization possible. Businesses can deploy AI agents tailored for niche responsibilities—essentially any niche responsibility, provided it is built on the right training data—without needing to overhaul their entire infrastructure. 

• Innovation 

Agentic AI can quickly analyze massive datasets, identify patterns, and test multiple solutions, accelerating discovery and problem-solving. In research-heavy industries, it supports experimentation, helping businesses uncover insights and refine strategies faster than human teams alone.

• Scalability 

Agentic AI can expand in scope as business needs evolve. It provides flexibility without requiring constant reconfiguration.

There is no denying that Agentic AI has the potential to change how businesses operate. Unfortunately, like any advanced technology, it comes with certain challenges: 

• Data and tooling 

Agentic AI depends on access to clean, well-structured data and the right tools to execute tasks. If data is incomplete, inconsistent, or outdated, the AI’s decision-making ability is compromised. Similarly, without proper integrations with enterprise software and APIs, agentic AI may struggle to act effectively within a business environment. 

• Workflow training 

If workflows are unclear or inconsistently followed, agentic AI will have a limited understanding of how to execute tasks. Without structured training data, the system will likely require significant human intervention to function correctly. 

• Cooperation and management of AI agents  

Many enterprise use cases involve multiple AI agents working together, but ensuring effective collaboration can be a challenge. A detailed orchestration system may be necessary to keep their interactions from becoming inefficient—or even counterproductive. 

• Autonomy and oversight  

Agentic AI can operate independently, but that autonomy must be balanced with human oversight. If left unchecked, AI can take actions that are not in harmony with business objectives or ethical guidelines. Organizations must implement safeguards to ensure AI-based decisions remain within established standards and in support of business objectives.

• Transparency and trust  

Agentic AI systems often operate as ‘black boxes,’ making it difficult to understand how decisions are made. When AI-driven actions lack clear explanations, trust in the system declines. Businesses need mechanisms for AI interpretability so users can validate the AI’s decisions. 

• Security and privacy  

Integrating agentic AI with enterprise systems increases exposure to some security risks. AI handling sensitive information must be protected against data breaches and unauthorized access. A strong, comprehensive IT security strategy should be in place wherever agentic AI operates.

• Generalization beyond narrow scopes  

Agentic AI performs well in defined environments but might struggle with some tasks that are outside its training data, producing inaccurate results or failing to adapt when needed. To mitigate this, organizations should establish safeguards that detect when AI is operating outside its expertise so that tasks can then be escalated to human decision-makers. 

• Explanation of decisions  

Without clear reasoning behind its choices, agentic AI can create confusion or make errors that are difficult to correct. Decision-making models must be structured in a way that allows users to trace AI logic. Without this, businesses may find it challenging to resolve unexpected behaviors.

Businesses today rely on a mix of automation tools, predictive analytics, and human oversight, but as demands grow, so does the need for systems that can handle complexity with less intervention. Agentic AI fills this gap—responding to tasks while also actively managing workflows and making strategic adjustments. This has made the technology a vitally important resource across diverse industries:

• Information technology  

Agentic AI can monitor information technology (IT) systems to detect performance issues and automate troubleshooting. It identifies patterns in system behavior, predicts potential failures, and applies corrective actions before disruptions occur, improving uptime and operational resilience. 

• Security and risk  

In IT security, agentic AI monitors networks for threats and responds to incidents in real time. It adapts security measures based on evolving risks, helping organizations stay ahead of threats while maintaining essential compliance standards. 

• Human resources  

Integrated with a human resource management system (HRMS), agentic AI can handle tasks like resume screening, employee onboarding, and workforce planning. It refines hiring recommendations and personalizes employee development based on evolving workforce needs. 

• Customer relationship management  

By integrating with customer relationship management (CRM) and customer service management (CSM) platforms, agentic AI enhances customer engagement by analyzing past interactions and predicting needs. It automates follow-ups and suggests solutions tailored to individual customer histories.

• Finance  

In financial operations (FinOps), agentic AI tracks financial activity, flags irregular transactions, and identifies cost-saving opportunities. It likewise helps finance teams manage budgets more effectively by analyzing trends and predicting future expenses. 

• Supply chain  

Agentic AI supports supply chain risk management by analyzing demand shifts and monitoring supplier performance. It detects disruptions and adjusts procurement strategies to keep operations running smoothly and consistently. 

• Application development  

Within the software development life cycle, agentic AI automates testing, detects bugs, assists with code optimization, and more. It is also a powerful tool in Agile development,  accelerating deployment by refining software based on usage data. 

• Customer service and employee support  

Agentic AI enhances customer service and employee management by automating routine inquiries and providing real-time recommendations. It helps customer service teams resolve issues faster and assists employees with HR-related tasks.

To function effectively, agentic AI moves through a structured process. Typically, this is defined by five stages:

Step 1: Indentifying & Gathering data 

Before agentic AI can act, it must first collect and process information from its environment. It pulls data from multiple sources, including databases, APIs, and real-time sensor feeds, developing a broad and up-to-date understanding of the task at hand. During this phase, it identifies key patterns, filters out irrelevant details, and organizes information so that it can be properly reviewed. 

Step 2: Reasoning 

Once the AI has processed the data, it moves into the reasoning phase. At this stage, it identifies patterns and evaluates relationships between data points. It may compare current conditions to historical trends, assess risks, or calculate probabilities; this allows it to refine its understanding before selecting the next course of action. 

Step 3: Building a plan 

Rather than executing an action immediately, agentic AI structures its tasks into a logical sequence. It prioritizes steps, considers potential obstacles, and determines the most efficient way to proceed. The system may also choose to revise its approach in response to changing conditions, ensuring that planned actions remain relevant. Dependencies between tasks are evaluated to prevent conflicts or inefficiencies.   

Step 4: Taking action  

Once a plan is finalized, agentic AI executes tasks through direct system interactions. It may adjust configurations, trigger automated processes, or request approval when necessary. Actions are carried out based on predefined policies to maintain accuracy and compliance. If unexpected results occur, the AI can pause execution and escalate issues for review. Built-in safeguards ensure that AI-driven actions remain aligned with business requirements.  

Step 5: Learning 

After completing a task, agentic AI reviews the outcome to determine whether additional adjustments are needed. It incorporates feedback from system logs and user interactions, and if errors or inefficiencies are detected, the AI updates its decision-making process for future scenarios. Over time, this iterative learning allows it to refine its performance without requiring manual reprogramming. A continuous feedback loop ensures that improvement is ongoing. 

Agentic AI represents nearly limitless possibilities, but how much the investment actually pays off depends on how effectively the technology is implemented. Organizations that take the right steps early on will be better positioned to maximize the value of their agentic AI solutions. Consider the following best practices: 

• Embed AI into operational processes  

Agentic AI does not work well in isolation; to be effective, it must fit within existing workflows. Connect the AI with enterprise applications, IT infrastructure, and process automation tools to ensure that decisions and actions support business operations. Just remember: AI should enhance—not disrupt—how work gets done.

• Leverage current enterprise infrastructure  

Rather than building standalone AI systems from scratch, businesses should integrate agentic AI into their established platforms. Embedding AI within enterprise resource planning (ERP), CRM, and IT management systems improves adoption and reduces unnecessary complexity.

• Define measurable goals and focus on impact  

Agentic AI performs best when given clear, structured objectives. Setting SMART (specific, measurable, achievable, relevant, time-bound) goals ensure that these objectives remain accessible. Organizations should also identify high-value areas where agentic AI can deliver the most tangible improvements. 

• Establish governance and oversight  

With increased AI autonomy comes a need for stronger governance, risk, and compliance (GRC). Companies should develop policies that define acceptable AI behavior, set risk thresholds, and ensure compliance with regulations. 

• Avoid fragmented AI implementations  

Standalone AI solutions may seem appealing but can create long-term inefficiencies. Disconnected platforms make it harder to manage AI-driven processes, leading to siloed decision-making. 

• Validate models and refine continuously  

Testing agentic AI in controlled environments enables businesses to detect weaknesses before full deployment. Organizations should simulate real-world scenarios and track AI performance under various conditions. A cycle of testing, validation, and improvement helps keep AI viable. 

• Structure decision-making for AI and human teams  

Agentic AI should complement human decision-making, and that means defining when AI acts independently and when human intervention is required. Decision scaffolding—such as approval checkpoints, escalation paths, and predefined limits—helps balance this oversight with the ever-present need for efficiency.. 

• Start small but iterate quickly  

Rather than attempting a large-scale AI rollout, businesses should begin with pilot projects. Testing agentic AI in controlled use cases allows for faster learning and scaling. Likewise, rapid iteration helps identify challenges early and ensures AI is optimized before full deployment. 

• Foster a culture of AI-driven learning  

In keeping with the theme of adaptability, organizations that embrace AI must also adapt their mindset. Encourage employees to engage with the AI and provide feedback, and offer training and other support to help those who are resistant to the technology become more comfortable using it. Companies that integrate AI into their learning culture will be better equipped to turn this innovation into a long-term strategic advantage. 

ServiceNow AI Agents bring agentic AI to life, empowering businesses to automate complex and time-consuming workflows, make intelligent decisions, and drive efficiency at scale. Native to the ServiceNow Platform^®, AI Agents integrate seamlessly with your enterprise data and systems, ensuring they have access to the real-time data, secure workflows, and the tools they need to  take action on behalf of people and drive exponential productivity .

ServiceNow AI Agents don’t just assist; they act, resolving IT issues, managing HR processes, optimizing customer service interactions, and handling operational tasks across all your teams and departments. The AI Agent Orchestrator makes this possible, coordinating multiple agents and allowing them to collaborate and execute multi-step processes with precision. And if you need a solution tailored to your business, AI Agent Studio lets you create custom AI agents using natural language rather than code. ServiceNow provides both out-of-the-box AI agents and the flexibility to build your own with full visibility and control. 

Deploying AI agents through ServiceNow means businesses can automate decision-making without sacrificing oversight. Built-in governance and analytics ensure that AI actions align with enterprise policies, while audit logging and security controls safeguard sensitive data. AI-driven insights power predictive issue resolution, helping teams address problems before they get out of hand. 

Simply put, ServiceNow AI Agents provide intelligent automation that scales with the needs of your business. Learn more about what AI agents can do for—demo ServiceNow today!