AI in ITSM tools

This phenomenon is very present in the software market, especially in IT Service Management (ITSM) tools. Within this sector, the concept of “Agentic AI” has recently gained traction. This concept can be confusing or even ambiguous when compared to the more familiar concept of “AI agents.” Although both terms are sometimes used interchangeably, and they share similarities, they are not exactly the same, as they represent different levels of automation and intelligence.

Differences between AI Agents and Agentic AI

AI agents are rule-based systems designed to execute specific tasks based on predefined inputs and objectives. They focus on automating repetitive tasks in controlled environments. Think of them as intelligent assistants that automate work efficiently, but remain limited by their programmed logic.

Examples of AI agents in ITSM include: a tool that automatically applies security patches at scheduled times; a chatbot that responds to users based on preconfigured options; or a monitoring system that triggers alerts based on defined rules and thresholds. A key limitation of AI agents is that they cannot “evolve” or adapt beyond their initial configuration, changes or improvements must be manually configured by humans.

Agentic AI, on the other hand, is not restricted to initial programming. It features autonomy and contextual adaptation, enabling it to decide the best action based on the situation. It has learning capabilities, improving over time, and with “awareness” of context can redefine its actions to optimally achieve its goals.

Examples of Agentic AI in ITSM include: a cybersecurity AI that dynamically adjusts security rules in real time based on detected attack patterns; a conversational AI that interacts naturally with users by querying internal knowledge bases and external sources, and can even identify knowledge gaps in the organization; or a monitoring system that not only redistributes processing/storage loads automatically but also redefines thresholds and actions based on historical behavior.

Why the distinction matters

Agentic AI represents the next stage in AI for ITSM, with contextual decision-making and continuous improvement, but it also requires robust machine learning models, which increases the cost of implementation. Because AI is now synonymous with innovation, some vendors market basic automation tools with “AI-powered software” labels to justify higher prices. This is not inherently wrong, basic automation may be sufficient and valuable for many organizations, but it is misleading to market it as advanced intelligence.

Benefits of Agentic AI in ITSM

Agentic AI offers very interesting applications in the ITSM field. Let’s explore some of these.

AI-Powered Self-Service for Request Resolution
Agentic chatbots can handle requests using natural, human-like conversation without feeling “robotic,” improving user trust and efficiently resolving issues without being limited to predefined responses.

Proactive Detection and Prevention
Agentic AI with visibility of IT architecture through a CMDB could detect anomalies and take proactive steps (e.g., switching to a backup server or rebalancing resources) before users notice disruptions.

Automated Problem Identification
Agentic AI can analyze historical incidents, find correlations, determine root causes and implement preventive actions much faster than human analysts.

AI-Driven Change Management
Agentic AI can evaluate risks of proposed changes, recommend optimal maintenance windows and automate low-risk changes to reduce business disruption.

Conclusions

While AI agents and Agentic AI share the same foundation (automation), they differ in scope and complexity, which also translates into differences in cost. When evaluating ITSM tools, it is essential to validate whether the price reflects true advanced automation or if the vendor is simply capitalizing on the “AI-powered” trend.

Although Agentic AI offers higher-level autonomy, not all organizations require such sophistication. Here, ITIL 4 guiding principles become relevant: focus on value, keep it simple and practical, and optimize and automate. For many organizations, basic automation is sufficient and appropriate until greater maturity is required.

Finally, Agentic AI is still a relatively new area, and concerns remain, especially the fear of job replacement. Technological disruption has replaced human tasks many times throughout history. This pushes academia to rethink educational programs to equip professionals not for routine technical tasks increasingly delegated to AI, but for roles that add strategic value in a world where AI is part of daily work.