When networks start thinking for themselves: The rise of agentic AI

Enterprise networks, the digital highways that connect people and data, have become intelligent nervous systems driving operations across hybrid cloud, software-defined wide area networks, IoT and 5G. Spanning continents, vendors and terabytes of data, they enable everything from video calls to factory automation.

Managing this complexity demands intelligence that learns, adapts and acts faster than humans. AI-native networks meet this need through automation, machine learning, GenAI and now agentic AI — the next leap toward autonomous network management.

The end of reactive network management

Network teams have long operated in a reactive loop: Alerts trigger an action, engineers investigate and a resolution follows, often after users have been affected. But today’s networks are too complex for that pace. The scale of the data and the number of devices and dependencies demand a smarter approach.

Organizations have already moved from reactive responses to proactive network management by automating manual tasks to improve speed, reliability and efficiency. This phase of rule-based automation has laid the groundwork for a deeper transformation. Now, AI is accelerating the shift by enabling predictive analytics and improving network visibility and responsiveness.

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What’s needed next is a network that thinks for itself — one that perceives patterns, predicts problems and acts proactively. This sets the stage for agentic AI, where intelligent agents act autonomously and make real-time decisions to manage and secure networks.

A smarter network starts with strong foundations

AI-native networks begin with foundational automation to execute core tasks faster and more accurately. When configurations, patches and policies run automatically, the network enters a self-healing state, building “muscle memory” that AI agents rely on.

AI can make smart decisions only if it trusts the underlying data and processes.

For example, automated workflows can push firmware updates to thousands of network devices, including routers and access points, without manual intervention. This ensures consistent network performance, enforces security policies and prevents configuration drift in distributed environments.

Machine learning: Seeing what humans can’t

Once the foundation is solid, it’s time to add intelligence. This is where machine learning and neural networks step in as problem-solvers. Every enterprise network produces a flood of data: logs, telemetry, events and alerts. The challenge isn’t to collect it but to make sense of it.

Machine-learning-driven analytics help teams cut through the noise by detecting patterns and predicting issues long before they affect users. The added intelligence leads to gains in productivity, efficiency and accuracy for your organization.

Consider these examples showing how machine learning strengthens network operations:

• Smart event clustering uses unsupervised learning to identify patterns, streamline root-cause analysis and automatically group related incidents. This reduces ticket noise — a high volume of unnecessary, redundant or miscategorized tickets — by up to 90%, NTT DATA reports show.
• Root-cause prediction models analyze historical data to preclassify new incidents, improving first-time resolution rates for Level 1 and Level 2 teams.
• Anomaly detection learns what “normal” looks like for every device, identifying deviations before they turn into deteriorated performance or outages.
• Usage forecasting uses intelligence, not just data, to predict when bandwidth thresholds will be breached and to allow for the reallocation of resources before customers notice.

The result is a calmer, more focused operations center that asks, “What’s about to go wrong next week, and how do we prevent it?” instead of scrambling to fix what just failed.

The impact is clear: Across NTT DATA clients, this approach has led to a 39% year-on-year reduction in P1 network incidents, the most critical type of outage.

And for your organization, it means lower costs, smarter capacity planning and happier users.

GenAI: Turning network data into conversation

Even with powerful analytics, there’s also the accessibility barrier. The insights are there, but they’re often buried in dashboards, reports and vendor portals. Getting answers can mean days of digging or waiting for specialized teams to compile and interpret them.

This is where GenAI changes the game. It makes complex network analytics accessible by turning raw telemetry, logs and alerts into clear, actionable insights. It empowers faster decisions, improves operational responsiveness and bridges the gap between network operations and business outcomes.

You can ask the network: “Which sites are experiencing recurring latency issues?”, or “Show me usage trends across all vendors this quarter,” or “Summarize this morning’s high-priority incidents.”

Within seconds, you get visualized, data-backed answers — no scripts, reports or delays. Your engineers are assisted by digital co-workers that summarize event timelines, suggest root causes and even recommend next steps. Meanwhile, managers benefit from instant visibility and insight, with no technical deep dives required.

Agentic AI: When networks think for themselves

Here’s where the story shifts from intelligence to autonomy — from smart networks to self-driving networks. If automation builds consistency, machine learning builds intelligence, and GenAI builds accessibility, then agentic AI builds autonomy.

This is where AI becomes an active partner. Agentic AI introduces specialized agents that perceive, decide and act — all within human-defined boundaries. Instead of replacing human network engineers, they support them by handling repetitive, time-critical tasks. This gives them time to innovate, design smarter architectures and focus on digital transformation.

At NTT DATA, we build and deploy AI agents that transform network operations, including:

• Health-check agents: Continuously test network health across devices and sites, identifying performance issues before they escalate
• Log intelligence agents: Scan and summarize key insights from system logs, saving hours of manual analysis and surfacing actionable information
• Process watchdog agents: Monitor adherence to operational standards, flagging deviations early to ensure compliance and consistency
• Carrier and site-coordination agents: Automatically manage service tickets, track resolution progress and confirm restorations to streamline multivendor coordination.

These agents both automate and collaborate. For example, when a link degrades, the diagnosis agent validates the issue with the health-check agent, confirms the impact and opens a prefilled incident ticket — all before users notice. This is agentic AI in action: A network that anticipates and prevents problems by acting faster and more precisely than any human team.

For your IT leaders, it means shorter incident lifecycles, consistent responses and predictable performance. For your organization, it delivers the confidence that your network can handle whatever the digital world demands.

But getting there requires more than tools. You need to strengthen your automation foundations, clean up your data flows and assess and adapt your network processes for AI-readiness. Moreover, defining clear roles and guardrails for agents is key to enabling autonomy with control.

The power of a platform

Automation, machine learning, GenAI and agentic AI are powerful individually, but their real value emerges when they’re unified. That’s the role of our One NTT DATA Platform — the digital fabric that unites these layers into a cohesive, self-improving system.

The platform integrates monitoring, data ingestion, model training and orchestration into a continuous loop. When machine learning predicts an anomaly, GenAI instantly surfaces it to engineers. When GenAI detects recurring patterns, agentic AI acts on them — closing the loop between insight and action. This orchestration reduces tool sprawl and simplifies governance.

For your organization, this delivers a lower total cost of ownership, unified visibility across vendors and architectures, and scalable AI adoption without re-engineering — always with human oversight in the loop.

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Balancing autonomy with accountability

As networks become more autonomous, trust becomes critical. Autonomy must come with accountability. Our approach to AI-native networking is responsible by design, guided by four core principles:

1. Security and privacy: Your data is processed securely in a private cloud environment, with each AI model operating in its own protected instance.
2. Transparency and explainability: Every action is traceable. Users can see when, why and how decisions were made.
3. Reliability and accuracy: Models are trained to understand your network topology, dependencies and real-world behavior.
4. Human oversight: All autonomous actions remain auditable, reversible and within human-defined limits.

Autonomy works when intelligence is paired with integrity. It’s how we make AI trustworthy.

The future is agentic

Forward-thinking organizations are transforming how they work by shifting from managing networks to co-creating with them.

At NTT DATA, we’re at the forefront of this revolution. We build AI-powered networks that supercharge business performance.

Are you ready to lead?