The era of agentic AI in private networks: From connected to ‘cognitive’

If 2024 was the year of proofs of concept and 2025 the year of deployment, 2026 is the year the “Day 2 scalability barrier” takes center stage.

Across industries — and particularly in manufacturing — we’re seeing a consistent pattern. Organizations have successfully set up private 5G networks, with active radios, a stable core and ubiquitous coverage. But as these networks transition from pilot projects to production, they’re hitting a wall.

This scalability barrier is a common but not insurmountable hurdle at this stage of maturity. It’s a sign that your network is ready for what comes next.

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The three biggest “Day 2” challenges

Industry 4.0 was not just about eliminating dead zones. It has also enabled massive increases in data volume and speed. From vibration sensors on computer numerical control (CNC) machines to high-resolution video streams from safety cameras, even a single smart factory now generates terabytes of telemetry every day.

Managing the network resources needed to move this data while also securing thousands of headless IoT devices — those that operate without a built-in user interface and are instead configured and controlled remotely — has outpaced the capacity of traditional IT teams. The barrier isn’t the bandwidth, but the operational overhead required to manage it.

Organizations struggle with three specific “Day 2” challenges:

1. The operational skills gap: OT teams are brilliant at manufacturing, but they’re not telco operators. Managing a 5G core, fine-tuning radio access network (RAN) parameters and handling spectrum interference requires niche expertise that most organizations don’t have in-house.
2. Brittle automation: Many early deployments relied on static scripts for automation. But factory floors are dynamic. If a metal shelving unit is moved, changing the radio frequency (RF) environment, static scripts fail. This leads to downtime and erodes trust in the investment.
3. The monetization gap: Most critically, business leaders are asking, “Where’s the ROI?” When private 5G is simply treated as “better Wi-Fi,” the ROI is hard to justify. You can’t monetize simple connectivity because connectivity is a commodity. You can, however, monetize guaranteed outcomes such as zero downtime for a business-critical automated guided vehicle (AGV) or guaranteed latency for a safety protocol.

The solution: Moving from automated to agentic

To transform a deployed network into a valuable one, we must shift our architectural philosophy. We need to move from automated networks — those that follow rules — to agentic networks that reason and act.

Unlike traditional automation, which waits for a human to approve a suggestion, agentic AI is a closed-loop system capable of executing the OODA loop — observe, orient, decide and act. An agentic AI agent is authorized to execute changes to the network in real time, preserving the digital thread as conditions change.

This opens the door to practical Day 2 solutions.

Dynamic network slicing for precision performance

In a traditional, policy-defined 5G slicing architecture, network slices are static partitions. An agentic model enables ephemeral network slicing. Instead of carving the network into fixed lanes, the network configures itself in real time to form tiny, purpose-built slices that exist only as long as they are needed.

A computer-vision camera on a factory floor needs minimal bandwidth for 99% of the day. But the moment it detects a potential safety hazard — a worker too close to a robotic arm, for example — an AI agent in the network core instantly reconfigures the RAN. It creates a high-priority network slice dedicated to that camera, guaranteeing crystal-clear, high-definition video for the next five minutes to ensure the control center sees every detail. Once the incident is resolved, the agent dissolves the slice and returns the resources to the pool. No human operator could type fast enough to achieve this.

The headless security guardian

OT environments are full of headless devices, legacy sensors and programmable logic controllers (PLCs) that can’t run modern security agents such as CrowdStrike or SentinelOne. This makes them vulnerable to cyberthreats.

Agentic AI acts as the network’s immune system. By analyzing traffic patterns at the packet level, agents can detect anomalies such as a temperature sensor suddenly trying to communicate with a server in a different country. The agent can autonomously quarantine that device at the network layer before the threat spreads, solving the security visibility problem that keeps CIOs up at night.

The path forward: From infrastructure to intelligence

As we navigate 2026, the distinction between “deploying a network” and “operating a nervous system” will define market leadership. For CIOs and network architects, the path forward involves three distinct strategic shifts:

1. Audit for autonomy, not just capacity

Instead of auditing your network solely on metrics such as throughput or uptime, start auditing your infrastructure for observability. Can your current network stack see the application layer? If your network can’t distinguish between a critical safety-video stream and a background operating-system update, it can’t be automated.

2. Trust the agent

The cultural shift is harder than the technical one. We must move from a human-in-the-loop model where humans approve every change to one where humans set the policy and AI executes the tuning. Start small by allowing agentic AI agents to manage noncritical optimization tasks, like load balancing during shift changes, to build organizational trust in autonomous operations.

3. Demand outcome-based models

Move away from vendor relationships focused on hardware procurement. The complexity of advanced 5G requires a partner, not a parts supplier. Look for network-as-a-service models where the vendor is incentivized on business outcomes such as guaranteed latency for AGVs, rather than technical inputs.

The era of manually tuning 5G networks is over. The competitive advantage now belongs to organizations that deploy cognitive networks — infrastructure that thinks, heals and adapts as quickly as the business does.

NTT DATA: Delivering the cognitive network

At NTT DATA, we recognized early on that the complexity of private 5G shouldn’t be your burden. That’s why we engineered our Private 5G as a Service platform to be AI-native from the ground up.

We deliver a managed outcome with an approach that integrates edge AI directly with the 5G core, enabling these agents to operate on-premises. This is crucial for our clients in defense and high-tech manufacturing who require absolute data sovereignty. The AI training data and network telemetry never leave their facilities.

The NTT DATA Technology Foresight 2026 report highlights the trend of “enhanced humans.” That is exactly what our private 5G offering enables. By offloading the minute-by-minute tuning of the network to AI agents, we free up your network engineers to focus on strategic innovation rather than firefighting.

We have proven this works in the most demanding environments:

• Celanese: In a complex chemical manufacturing environment, where reliability is a matter of physical safety, we deployed a private 5G network that covers 99.9% of the facility. This isn’t a lab; it’s a hazardous, brownfield reality where our network enables Industry 4.0 applications to run without interruption.
• City of Las Vegas: We deployed one of the largest private networks in the US. If our architecture can use AI to manage the chaotic data of a smart city — balancing traffic sensors, public safety video and municipal services — it can manage the deterministic environment of a factory floor.

If you’re ready to move beyond the Day 2 scalability barrier and turn your private 5G network into an autonomous innovation engine, let’s discuss the architecture of the future.

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