How Agentic AI Will Reshape Global Network Infrastructure

Agentic AI—software systems that autonomously perform tasks using AI models—is poised to reshape global network infrastructure. According to Cisco’s research on the impact of AI on wide area networks (WANs), the shift is not just about more traffic. AI agents change the behavior of network traffic itself, forcing enterprises and service providers to rethink how networks are designed and managed.

Instead of occasional user queries, agentic systems continuously interact with models, data sources, and other services. The result is a dramatic increase in network load and a fundamental change in traffic patterns.

Why AI Agents Generate Far More Traffic Than Humans

Cisco describes AI agents as “network power users.” Unlike humans—who typically perform discrete actions such as loading a web page or sending a message—agents operate continuously at software speed.

In practice, that means a single agent workflow can:

• Send repeated inference requests to AI models
• Pull data from multiple APIs and cloud services
• Exchange tokens and intermediate results across services
• Trigger chained workflows that call additional models or tools

Because of this constant activity, Cisco estimates AI agents can generate around 450% more network traffic than human users in comparable workflows.

Another key difference is traffic direction. Traditional internet usage is mostly download‑heavy (for example, streaming video or loading web pages). AI workflows, by contrast, involve continuous bidirectional exchanges, with significant upstream traffic as prompts, data, and intermediate results flow to models before responses return.

The Real Driver: AI Inference, Not Training

Public discussion about AI infrastructure often focuses on training large models. Cisco’s study highlights a different reality for networks: inference dominates operational traffic.

Inference refers to the real‑time use of trained models—answering questions, generating content, analyzing data, or executing agent tasks. Cisco’s research is based on measured inference traffic on service‑provider networks, showing that these live workloads are the primary driver of WAN demand.

The report projects that inference traffic alone could drive 63% additional network growth compared with projections that exclude agentic AI workloads.

A Major Shift in Global Traffic by 2035

If agentic AI adoption follows Cisco’s projections, its network footprint will become substantial over the next decade.

Key forecasts include:

• AI inference could reach about 25% of all network traffic by 2035.
• The largest acceleration is expected between 2029 and 2032, when enterprise adoption of AI agents is predicted to surge.

This growth reflects a structural shift: instead of occasional bursts of traffic when a user queries a model, AI agents create continuous workloads that keep networks busy around the clock.

Why Enterprise Network Traffic Could Grow 9×

Cisco also modeled how AI adoption may affect enterprise traffic volumes.

Without agentic AI, enterprise network demand from 2026 to 2035 would likely grow about 2.5×, driven by cloud services, SaaS applications, and digital transformation.

With widespread agentic AI, however, the same period could see traffic grow to roughly nine times today’s levels, fueled by autonomous workflows that repeatedly access data, models, and services.

These systems effectively multiply activity across the network because each human task can trigger many automated interactions between services and AI models.

How AI Traffic Changes Network Behavior

Cisco’s research stresses that AI doesn’t simply increase traffic volume—it changes traffic characteristics.

Compared with conventional web or SaaS traffic, AI workloads tend to be:

• More dynamic: AI agents initiate bursts of requests and distributed workflows.
• More latency‑sensitive: inference delays directly affect user experience and agent performance.
• More bidirectional: upstream traffic rises as prompts, telemetry, and intermediate data move to models.
• More complex: workflows often involve multiple clouds, APIs, and inference endpoints.

These changes make traditional network planning—based on predictable user activity—much less reliable.

Why Networks Need New Architecture for AI

Because AI inference paths directly affect application quality, Cisco argues that networks must treat them as critical infrastructure, not ordinary traffic flows.

This shift implies several architectural priorities:

1. AI‑Aware Traffic Engineering

Inference flows may require prioritized paths, latency guarantees, and dynamic routing decisions to maintain performance.

2. Distributed Compute and Edge Placement

Placing inference infrastructure closer to users or data can reduce latency and prevent WAN congestion.

3. Real‑Time Observability

AI workloads produce fast, complex traffic patterns, making end‑to‑end visibility essential for detecting congestion or degraded performance before workflows fail.

4. Stronger Resilience

Cisco research highlights that network outages already cost businesses heavily and are often triggered by congestion, cyberattacks, or configuration errors. AI workloads increase pressure on networks that must remain continuously available.

The Bottom Line

Cisco’s WAN research points to a deeper transformation than simple bandwidth growth. Agentic AI introduces always‑active, inference‑driven workloads that behave differently from traditional internet traffic.

If adoption trends follow current projections, networks over the next decade will need to support:

• AI agents acting as high‑frequency network users
• large volumes of real‑time inference traffic
• more symmetric and latency‑critical flows

That means future network design will increasingly revolve around AI‑aware architectures—combining higher capacity, distributed compute, advanced telemetry, and specialized quality‑of‑service for inference traffic.

Even so, these projections remain forecasts based largely on Cisco’s measurements and modeling. The exact scale of the shift will depend on how quickly agentic AI becomes embedded in everyday enterprise and consumer workflows.