Agentic AI is changing how IT is managed. Here's what to know.

Imagine a network monitoring system that doesn’t just alert you about anomalies but actively troubleshoots and resolves issues in real time—without human intervention. That’s the promise of agentic AI. According to Gartner, by 2029, 80% of typical customer service problems will be resolved without the need for human intervention thanks to AI-driven autonomous agents.

What is agentic AI?

Agentic AI refers to AI systems that can operate with a level of autonomy, making decisions, initiating actions, and adjusting strategies based on environmental inputs. Unlike traditional AI—which primarily follows predefined rules—agentic AI is designed to act independently within a defined scope, mimicking human-like decision-making.

Agentic AI architecture simplified:

The architecture of agentic AI is built on a layered system designed to process vast amounts of data, analyze patterns, and execute tasks autonomously. At a high level, it comprises several interconnected components that enable intelligent decision-making.

• Perception module: This module collects and processes data from various sources, such as network logs, application performance metrics, and external threat intelligence feeds. It uses advanced machine learning techniques to filter out noise and focus on relevant insights.
• Cognition a.k.a. reasoning engine: Once data is gathered, this engine applies probabilistic models, reinforcement learning algorithms, and decision trees to determine optimal responses. It continuously refines its models based on past outcomes, allowing the AI to learn from experience and improve accuracy over time.
• Action mechanism: The AI executes remediation tasks, such as adjusting firewall rules, reallocating server resources, or rerouting network traffic to prevent failures. These actions are guided by predefined policies and adaptive learning models that enable rapid response to emerging threats and performance issues.
• Feedback loop: This component ensures continuous learning by evaluating the impact of each action and fine-tuning future responses. It integrates with observability tools to assess system health and refine AI-driven automation, enabling more context-aware decision-making.

How agentic AI works with a network monitoring solution

Agentic AI enhances network monitoring solutions by enabling proactive management and automated issue resolution. It follows a structured workflow to detect and mitigate anomalies:

1. Anomaly detection:The AI continuously scans network traffic, system logs, and application performance metrics to identify unusual patterns, such as sudden spikes in latency or unauthorized access attempts.
2. Root cause analysis (RCA):Using historical data and predictive analytics, the AI determines the underlying cause of each anomaly. For example, it may correlate an increase in failed login attempts with a potential security breach.
3. Automated remediation:Based on predefined policies, the AI takes corrective actions such as blocking suspicious IP addresses, reallocating resources to prevent server overload, or reconfiguring network settings to optimize performance.
4. Escalation and notification:If an issue exceeds the AI’s resolution capabilities, it escalates the problem to IT teams with a detailed report, ensuring swift human intervention where necessary.

How has agentic AI evolved?

The past: Rule-based automations simplify IT management

In its early days, AI in IT operations was limited to rule-based automation. These systems followed strict predefined logic, requiring manual updates whenever new threats or system behaviors emerged. While useful, they lacked adaptability and struggled to keep up with the evolving IT landscape.

The present: Deep learning and NLP ensure efficiency and accuracy

Modern agentic AI leverages deep learning, natural language processing (NLP), and real-time data analytics to perform complex tasks with minimal human oversight. It can analyze vast amounts of IT telemetry data, recognize patterns, and autonomously act to resolve issues. The adoption of AI-powered observability platforms has further strengthened the capabilities of agentic AI, allowing for more precise and context-aware decision-making.

Organizations are also exploring single-agent and multi-agent systems within agentic AI. Single-agent systems operate independently, handling specific tasks such as anomaly detection or compliance monitoring. Multi-agent systems, on the other hand, consist of multiple AI agents that collaborate, share data, and delegate tasks dynamically to optimize network performance and security. Multi-agent architectures improve scalability, enabling AI to handle distributed IT environments more efficiently.

The future: Multi-agent systems and better communication

The future of agentic AI lies in reinforcement learning and collaborative multi-agent systems. Future iterations will involve AI agents that communicate and coordinate across different IT environments, enabling fully autonomous IT operations. With the rise of AI-driven DevOps and self-healing infrastructure, enterprises can eventually expect near-zero downtime and highly efficient IT ecosystems. Emerging models will also incorporate federated learning, allowing AI to train on decentralized data while preserving security and compliance.

AI agents vs. agentic AI

Agentic AI benefits

Organizations across industries are embracing agentic AI for its ability to drive efficiency, security, and automation in IT operations. Here are some key benefits:

1. Proactive problem resolution:

   Traditional IT monitoring tools are reactive, meaning they only alert administrators after an issue occurs. Agentic AI, however, can predict system failures based on trend analysis and automatically implement preventive measures before they impact operations.

   Use case: In financial services, agentic AI predicts system failures and automatically deploys fixes before downtime impacts trading platforms, reducing operational risks.

2. Enhanced security and compliance:

   Cyber threats evolve constantly, and static security policies are insufficient. Agentic AI enhances security postures by continuously analyzing network behavior, detecting anomalies, and implementing corrective actions in real time.

   Use case: In healthcare, agentic AI ensures patient data compliance by monitoring access patterns and preventing unauthorized access, thereby meeting HIPAA and GDPR regulations.

3. Optimized IT resource allocation:

   By analyzing workload trends and infrastructure utilization, agentic AI dynamically reallocates IT resources to ensure optimal performance and cost efficiency.

   Use case: In manufacturing, agentic AI distributes computing power across workloads, reducing energy costs and enhancing operational efficiency.

4. Real-time anomaly detection:

   Network performance issues and fraudulent activities often go unnoticed until they cause significant damage. Agentic AI detects anomalies in real time and mitigates potential risks before they escalate.

   Use case: In retail, agentic AI monitors POS systems for fraudulent transactions and blocks suspicious activity instantly, reducing financial losses.

5. Seamless workflow automation:

   Agentic AI streamlines complex IT workflows by automating repetitive tasks and integrating with IT service management (ITSM) solutions.

   Use case: In telecom, agentic AI automates network configurations based on usage trends, ensuring uninterrupted service and minimizing manual intervention.

Key takeaway

By adopting agentic AI, enterprises can enhance operational resilience, reduce IT overhead, and improve service reliability across various industries.