The AI infrastructure challenge: When traditional monitoring isn't enough

As AI transforms business operations, network infrastructure faces new challenges. The performance of the network will impact performance of AI for critical workloads, yet we are still using the same traditional network and service monitoring approaches.

Network service performance has traditionally been measured using Layer 3 (IP, such as delay, packet loss, and jitter) and Layer 4 (TCP) and even Layer 7 (HTTP) metrics. As we move into the era of agentic AI, a single request from a human or an API might generate hundreds of interactions between AI agents and large language models (LLMs). This means we need to look at what we measure to determine network performance and how we measure it.

Nearly seven in ten companies (69%) rank AI as a top IT budget priority in the Cisco AI Readiness Index survey of global businesses (Realizing the Value of AI: Cisco AI Readiness Index 2025). Rising AI workloads are expected to stretch infrastructure for all, with 63% of businesses anticipating AI workloads will rise more than 30% in the next two to three years.

Why AI traffic calls for network assurance to adapt

As AI becomes increasingly central to the running of an organization, the ability to ensure end-to-end service levels of AI workflows becomes critical. This will include the performance of training or inferencing clusters within a data center, or interactions over a WAN between inferencing and LLMs, to AI agents that are collaborating on an autonomous process.

As AI adoption accelerates, the transport network becomes a mission-critical foundation. We need to understand the role the network plays in the performance of AI workloads, and assure the network is delivering the performance required so that those workloads are performing up to customer expectations. Traditional monitoring tools don't provide enough granularity to detect issues that will impact the performance demands of AI traffic, and they don't measure the performance of the AI agents and the LLMs they are interacting with over the network.

Five ways to make your network assurance AI-ready

How ready is your network assurance to cope with the demands of AI traffic? Here are the five ways proactive assurance solutions can adapt to assure AI workload performance and ensure your other network traffic is not negatively impacted.

1. Establish AI-specific performance baselines
Continuous proactive assurance of traditional network metrics at Layer 3 provides latency, jitter, throughput, and packet loss metrics to benchmark performance for inference, training, and agent traffic. It flags anomalies early before AI processing is disrupted. In addition, we need to understand how the network conditions impact the performance of agents and LLMs across the network. This requires that we develop new LLM metrics to make certain you are measuring the request latency, time to first token, and time per output token (i.e., the amount of time taken to generate each token after the first token or inter-token latency).

2. Provide AI-centric WAN and path analytics
With real-time path visibility and intelligent telemetry, assurance verifies that AI traffic across data centers, edge nodes, and public or private clouds meets service level agreements (SLAs). This is critical for retrieval-augmented generation (RAG) workflows, model sync, and distributed AI operations.

Figure 1. As AI agents and LLMs evolve, network latency becomes more critical

3. Correlate LLM agent performance with network conditions
Assurance sensors track how the interactions between agents and LLMs behave under varying network conditions. When performance is impacted, assurance analytics helps pinpoint whether the performance issue relates to the model or whether the network is the root cause. This speeds up mean time to resolution (MTTR) and mitigates blame-shifting among vendors or teams.

4. Optimize resources and enforce SLAs dynamically
Using policy-based automation, assurance can help in intelligently routing AI workloads according to performance needs. It mitigates microbursts, enforces quality of service, and ensures inference traffic gets priority. Optimization also includes intelligent routing of requests to the LLM that best meets the performance requirements, such as time to first token, request latency, tokens per second, failure rate, and so on. This visibility is critical but so, too, is ensuring you have full observability of LLM transactions, LLM redundancy and switchover, load balancing, semantic and cost optimization, regulatory compliance, and guardrails and security.

5. Future-proof operations with open, automated assurance
Designed for agility, assurance solutions need to be adaptive and open to support new telemetry sources and assurance models. Assurance AIOps, cloud orchestrators, and federated cross-domain data together enable closed-loop, AI-aware network automation. Emerging AI agent frameworks will further drive autonomous networks with minimal human oversight. The foundational element for agentic AI architecture is the pretraining of large data sets that create general purpose LLMs that are "fine-tuned" using additional domain-specific data to create domain-specific or job-specific LLMs.

Measuring the network performance and the performance of the LLMs and agents running over the network ensures that you have visibility into the critical factors that will impact the performance of AI workloads.

How Cisco Provider Connectivity Assurance enables AI-ready networks

Cisco Provider Connectivity Assurance helps assess whether networks are "AI-ready." The solution is evolving to incorporate AI-specific WAN performance testing for inference, RAG, and agent-based operations. It also introduces LLM agent performance sensors that enable correlation between large language model agent behavior and underlying network performance.

For example, Provider Connectivity Assurance can help identify, categorize, and localize network traffic generated by AI workloads and applications and provide assurance for AI-focused services across the network. Provider Connectivity Assurance also makes it possible to simulate user or agent behaviors by testing LLM performance and correlating this with the underlying performance of the network.

Full visibility of the transport network is important. Provider Connectivity Assurance AIOps provides the multilayer network visibility that is required along with the correlation of the multiple layers: optical, nodes, links, and paths together with AI user experience.

Getting started: Assess your AI readiness

Before making your network assurance checklist, evaluate the current state of your network:

• Can your monitoring solution detect sub-second traffic anomalies? AI microbursts happen in milliseconds-traditional five-minute polling intervals won't catch them.
• Do you have visibility into LLM-specific performance metrics? Metrics like time to first token and inter-token latency are critical for AI application performance but invisible to conventional tools.
• Can you correlate application performance with network conditions in real time? When LLM performance degrades, you need to know immediately whether it's a model issue or a network issue.

If you answered "no" to any of these questions, your network assurance may not be ready for the demands of AI workloads. The good news? Purpose-built solutions like Cisco Provider Connectivity Assurance can bridge these gaps and prepare your infrastructure for AI at scale.

See Cisco Provider Connectivity Assurance in action. Request a live demo to discover how it makes your network AI-ready.

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Related blog: Achieving Reliable AI Models for Network Performance Assurance