AI-powered IT agents have transitioned from a theoretical concept to a practical reality in today’s enterprise environments. As organizations rapidly adopt generative AI, these intelligent systems deliver tangible results, especially in cybersecurity, where speed, accuracy, and automation are crucial. AI and automation now serve as foundational pillars for building cyber resilience across complex IT landscapes.
According to Forrester, 67% of AI decision-makers plan to increase their investment in generative AI by 2025, with IT operations and cybersecurity identified as top priorities. AI has helped reduce incident response times by up to 30% in live environments, leading to improved outcomes without increasing headcount (Bono et al., 2024).
However, the effectiveness of AI agents relies on the quality of the information they utilize. These systems must maintain continuous, real-time visibility into the connections between infrastructure and applications. ServiceNow emphasizes this point, warning that AI tools risk operating without context without a clear understanding of dependency relationships, which can introduce errors or inefficiencies.
What AI Agents Are Already Doing in Real Environments
Here are four practical capabilities that AI agents are currently providing in IT environments and cybersecurity operations.
1. Accelerate Incident Resolution: AI agents can significantly shorten the time required to resolve critical incidents by identifying root causes and implementing corrective actions before human intervention is needed. These agents typically operate in the background, scanning system logs, detecting anomalies, and automatically triggering workflows to neutralize issues
For instance, Microsoft Azure AI Agents utilize Copilot to analyze logs and suggest specific remediation steps without requiring human input. This capability allows IT teams to concentrate on the few incidents that truly need expert intervention, thereby reducing the mean time to resolution (MTTR) across the board. In SOCs, this speed is crucial for neutralizing threats before they escalate.
2. Prevent Failures with Predictive Monitoring: AI agents address issues after they arise and work to prevent them from happening in the first place. They can predict anomalies several days in advance by analyzing historical data and usage patterns. This allows teams to address potential breakdowns before any disruptions occur.
For example, ServiceNow’s predictive AIOps platform utilizes machine learning models to monitor performance degradation across business services. It alerts IT teams or takes autonomous action based on early warning signs of trouble. This proactive approach is especially valuable in cybersecurity, where preventing service outages is crucial for maintaining defense perimeters.
3. Optimize Infrastructure in Real-Time: Cloud environments are dynamic, and AI agents play a crucial role in optimizing resource allocation. They help minimize waste and ensure high availability without overspending.
CAST AI’s Autoscaler for Azure Kubernetes Service continuously assesses cluster performance, automatically scaling resources up or down based on actual demand. This is a clear example of how AI and automation support cyber resilience. By right-sizing virtual machines and balancing workloads, these agents effectively reduce unnecessary costs while maintaining performance. When combined with application dependency mapping tools, the optimization becomes even more precise, allowing agents to make informed decisions based on the behaviors of interdependent systems.
4. Stay Audit-Ready Without the Rush
Preparing for cybersecurity audits has traditionally been a labor-intensive process. However, AI agents now automate much of this work by continuously tracking user activity, system changes, and configuration updates.
Platforms like Cyble integrate these tracking mechanisms directly into IT operations. As changes occur, logs are generated, stored, and made available for real-time or periodic audit reviews. This automation not only reduces the compliance burden but also ensures organizations are always ready to demonstrate adherence to internal and external security standards like ISO 27001 or NIST frameworks.
For highly regulated industries, this continuous readiness for audits lowers risk exposure and provides peace of mind to compliance teams and executives alike.
Why Visibility Still Matters
All these benefits rely on a crucial foundation: visibility. AI agents need access to high-quality, real-time data about the environments they operate in. Without accurate data, their decisions can be flawed or even dangerous. This is where application dependency mapping (ADM) plays a vital role.
ADM provides a comprehensive view of how business services, systems, and applications interact, delivering the context-driven visibility cybersecurity teams need to eliminate blind spots and make smarter decisions. This context enables AI agents to understand cause-and-effect relationships across complex environments, preventing misguided automation and enabling more accurate, reliable responses.
Final Thoughts: Build Security Intelligence into Your Infrastructure
AI agents are no longer just a future promise. They are actively helping IT and cybersecurity teams detect threats faster, prevent outages, and reduce the burden of compliance. But for AI to work effectively, it needs more than data. It needs a real-time, complete understanding of the systems it is designed to support and protect.
Even the most advanced AI systems will operate on assumptions and incomplete information if the environment is fragmented or lacks clarity. That can lead to missed threats, misallocated resources, or compliance risks. Mapping your entire digital footprint, including all systems and connections, ensures that AI agents have the context to act reliably and securely.
To fully harness the benefits of AI in IT, organizations must ensure that their agents are well-informed and stop expecting AI to succeed without a complete understanding of their environment. Only by having live, accurate visibility and a comprehensive picture of their operations can they enable AI to deliver faster responses, enhance operations, improve security outcomes, and achieve measurable impact.
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Author Bio: Ofer Regev, CTO and Head of Network Operations at Faddom
Ofer has 18 years experience in the IT industry. He currently serves as CTO and head of network operations for Faddom (formerly VNT), a startup that raised $12 million to help companies map IT infrastructure wherever it lives. Faddom is used to map and monitor over 1 million application instances at organizations like Coca Cola, NetApp, and UCLA. He previously served in the IDF’s elite computing and information services unit, Mamram.
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