Agentic AI Memory Systems Are a Bellwether for Network Traffic Growth

Enterprises need to know the operational impact of the agentic AI solutions they deploy. Agentic memory is currently experiencing accelerated innovation, making it the next bellwether for operational impact. Why? Innovation in agentic “long memory” is directly accelerating innovation in agentic AI, and agentic AI directly impacts the network.

According to a recent report by S&P Global Data, “Advances in reasoning, multi-agent systems, and retrieval are driving agentic AI: Agentic AI is rapidly evolving through advanced reasoning frameworks, dynamic multi-agent collaboration models, and intelligent retrieval techniques. These innovations enable agents to autonomously perceive, plan, and act, enhancing scalability, adaptability, and personalization in complex, real-world environments.”

Advances in long memory technology are fueling innovation in agentic AI. Also known as advanced reasoning frameworks, options such as LangMem, Memobase, and Mem0 utilize new types of memory function that provide access to state, context, and evolution of information during and between agentic flows. All this creation, storing, updating, moving, and sharing of data increases network demand.

Further, agentic memory systems constitute a new data location potentially accessed by groups of agents that need to share and update information—enterprise and personal—which must be maintained, governed, audited, and secured with the same rigor as any other enterprise asset. This reality uncovers three impacts to network traffic.

According to the Nokia Global Network Traffic Report, enterprise AI traffic is expected to grow at a compound annual growth rate (CAGR) of 57% through 2033. Why? Agents introduce new traffic that did not exist prior to their deployment, increasing bandwidth consumption and the potential for latency. That traffic shows up as API calls. More actions taken by more agents means more API calls, impacting the responsiveness of API endpoints. Retrieval-augmented generation (RAG) is another source of API calls likely to increase with adoption of agents because dynamic retrieval memory brings awareness of changing context. This means RAG will no longer be static but rather updating in real-time. More RAG means more API calls to vector databases to update data as well as to enrich inference.

IDC reports that enterprises are aligning their GenAI roadmaps with network modernization efforts to support agentic AI workloads. More network traffic pathways are emerging in all directions, creating a data path mesh that grows exponentially as more agents and resources are added. More container-to-container traffic. More traffic into and out of container hosts. More traffic into and out of container pods and clusters. Increased network pathways require additional policy, additional configuration to network components, and additional automation. Network maps become busier, and monitoring activity increases. The result? All of these culminate in a larger environment, a data path mesh, subject to maintenance, security, and governance processes.

More pathways require more emitting and collecting of operational telemetry. This telemetry supports all types of observability—from operational management and troubleshooting to security and governance. High concentrations of agents, models, and resources can create network congestion in centralized architectures. Intelligent routing can mitigate this to some degree, but it requires balancing congestion in one segment of the network with that of another, which may not actually help if the capacity of the network is not sufficient.

Fortunately, enterprises can apply lessons learned from the adoption of microservices to agentic AI. The similar explosion of operational telemetry leads to the same cost control measures such as deciding how much of which telemetry matters to emit/collect/analyze/store.

Agentic AI is still in the build phase. While developers create tools and development kits, IT operators need to prepare for the inevitable impact on their network.

The best prepared organizations will have combined developer prototypes with early-stage infrastructure testing to monitor and measure network impact so that enhancements can be put in place in time for production deployment. Collaboration and communication itself will reduce the risk of one function getting ahead of another. In addition, tracking the pace of innovation in agentic memory will help network operators stay ahead of the innovation curve.

Once agentic memory innovation settles down, IT operators will know that the time is getting closer for the first enterprise-scale agentic solution deployment on to their network.