Agentic AI: The Power of Intelligent Automation in IT Asset Operations

If Generative AI explains and recommends, Agentic AI acts.

Imagine a world where your IT assets don’t just sit in a database waiting for someone to notice a problem. Instead, the system itself anticipates needs, makes reasoned choices within clear boundaries, and executes actions. That’s the promise of Agentic AI, and it’s already moving from concept to reality in leading organizations.

What Is Agentic AI?

Agentic AI is an artificial intelligence system that achieves a specific goal with limited human supervision. It comprises AI agents—machine learning models that mimic human decision-making—solving problems in real time by evaluating context, selecting actions, and executing tasks.

In IT Asset Management, Agentic AI can perform asset-related tasks across workflows based on predefined rules, permissions, and approvals, etc., rather than just generating text or suggestions.

Unlike traditional AI models, which operate within predefined constraints and require human intervention at each step, Agentic AI exhibits the following:

• Autonomy (acts independently within guardrails)
• Goal-driven behavior (optimizes toward outcomes, not outputs)
• Adaptability (responds to changing environments)

The term “agentic” refers to this agency - the capacity to act independently and purposefully.

The Applications of Agentic AI in IT Asset Operations

Agentic AI introduces a shift in IT asset management: from tools that record and report to systems that can decide and act within defined rules. While traditional ITAM automation handles predefined tasks (such as scheduled scans or alerts), Agentic AI goes a step further by evaluating context, choosing the next action, and executing workflows across systems.

Below are practical areas where Agentic AI can make a real impact in ITAM.

Intelligent and autonomous asset management

As device volumes grow, maintaining accurate asset records becomes increasingly complex. Modern ITAM tools already automate data imports from sources such as network scans and endpoint management systems (like Intune), and they trigger reminders for warranties or scheduled maintenance.

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Agentic AI brings this much further.

Instead of simply storing records or raising alerts, AI agents continuously evaluate usage patterns, performance signals, patch status, and lifecycle compliance under reliable data inputs in place. When anomalies are detected, the agent analyzes them in context, comparing them against security policies, lifecycle rules, cost thresholds, and user risk profiles.

From there, the agent determines the appropriate response path. For low-risk scenarios within predefined policy limits, such as routine patch deployment or maintenance scheduling, the agent can initiate automation directly. For higher-risk, cost-sensitive, or policy-impacting actions, it generates a recommended plan and routes it to administrators for review and approval before execution.

Beyond risk prevention, agents also optimize resource allocation, ensuring assets are available when needed while avoiding unnecessary stockpiling. The shift is clear: from passive IT asset tracking to active asset lifecycle optimization with intelligent automation.

Intelligent software license allocation

Software remains one of the most expensive asset categories in most organizations, with underutilization leading to significant financial waste. Traditional ITAM tools already help by tracking license usage and monitoring SaaS consumption. But Agentic AI can transform this into a closed-loop license management system, where it can:

• Allocate licenses automatically when users join or change roles
• Reclaim licenses when they are no longer used
• Analyze licensing terms and compare them with real deployment data
• Identify compliance risks before they escalate

So, instead of reacting to audits, organizations move toward continuous compliance with Agentic AI.

Intelligent change and configuration management

Change management is often time-consuming and risk-sensitive. AI agents enhance this process by proactively evaluating the potential impact of proposed changes across the IT environment.

By analyzing relationships between configuration items (CIs) in the CMDB, agents can identify hidden dependencies and recommend the optimal timing for implementation based on infrastructure usage patterns. Low-risk, routine changes can be executed automatically within governance boundaries, while high-impact changes remain under human control.

Where Agentic AI Is Already Acting in IT Asset Operations

We’re not waiting for the future; it’s already here. One of the clearest production examples of agentic behavior in IT Asset Management can be seen in hardware request fulfillment workflows enhanced by Now Assist for Hardware Asset Management from ServiceNow.

The traditional process of hardware request fulfillment

Traditionally, hardware request fulfillment follows a structured but heavily manual process. An employee submits a request through the service catalog. A manager approves it. The request is then assigned to a procurement user, who determines how to source the asset — by consuming local stock, transferring inventory from another stockroom, or purchasing from a vendor.

Many systems may provide tools to easily visualize the stock level, availability, etc., but every sourcing decision is made by a human inside the hardware asset workspace.

Now Assist introduces AI agents into this flow, transforming it from manual coordination into goal-driven orchestration.

How Now Assist AI Agents handle hardware sourcing

The process still begins the same way: an employee submits a hardware request, and once approved, the request moves to procurement.

But instead of waiting for a person to evaluate inventory, the workflow now triggers AI agents.

The first is the Hardware Asset Management Sourcing Agent. Its objective is simple: fulfill the request using the most efficient available option. Here’s how it reasons through the task:

• If local stock is sufficient and automation is enabled, it consumes inventory automatically.
• If additional units are required, it triggers a Transfer Order Creation AI Agent to move assets from another stockroom.
• If inventory is still insufficient, it activates a Purchase Order Creation AI Agent to source assets from a vendor.

The level of autonomy is configurable. The team can choose to let the system operate in a fully automated mode, where the AI agents automatically consume stock, create transfer orders, or generate purchase orders without human involvement.

Or they can opt for a more controlled approach. In this case, the AI agent first prepares a sourcing plan and presents it to the procurement user for review. The user can approve it, adjust it, or reject it before any action is taken. In other words, humans define the boundaries, and the AI executes within control.

This Reflects the Early Production Signals of Agentic IT Operations

This workflow represents more than automation. The AI Agents:

• Understand the goal (fulfill the hardware request)
• Evaluate real-time stock conditions
• Decide between multiple sourcing paths
• Coordinate sub-agents (transfer and purchase)
• Adapt based on configuration and human feedback
• Execute actions across systems

Rather than triggering predefined tasks, the system reasons through the fulfillment process and dynamically selects the optimal path. The result is reduced manual workload, faster resolution times, improved asset utilization, and higher employee satisfaction.

This is not full autonomy across all IT asset domains yet, but it is a clear production signal of where agentic IT operations are heading.

What’s Next for AssetLoom

Agentic AI is not a distant concept. It’s the natural evolution of intelligent IT operations. As an advanced IT asset operations platform, AssetLoom approaches Agentic AI not as a standalone feature, but as an operational layer that works across workflows, policies, and lifecycle management. This is part of our roadmap vision.

We’re exploring how autonomous ITAM agents could one day interpret policies and assist with onboarding and offboarding workflows. We’re also investing in natural language interfaces that make asset insights accessible beyond IT teams. And we see strong potential in AI-driven prioritization, which helps organizations focus on the tasks that carry the highest financial and compliance impact.

This isn’t about replacing human decision-making. It’s about strengthening it with intelligent systems that can adapt, reason, and support execution at scale. The AI evolution in IT Asset Management is already underway, and AssetLoom is building toward that future.