Traditional capacity planning won’t work in the era of agentic AI
As AI becomes core to enterprise operations, campus and branch network architects are stepping into new territory. The old playbooks for capacity planning won’t hold in the age of agentic AI, where autonomous systems interact, collaborate, and decide in ways that defy traditional, predictable traffic patterns.
Enterprise AI is no longer confined to the data center. AI models, inference, and autonomous agent-to-agent exchanges are now running at the edge, on campuses, and in branch networks. This shift is setting entirely new requirements for visibility, adaptability, and capacity planning.
When we’re no longer able to predict capacity, what does it mean to be AI-ready from a network perspective?
From client–server to agent–agent
Historically, enterprise networking followed a client–server model. Whether accessing SaaS, joining a video call, or querying internal CRM data, traffic flowed predictably between an endpoint and a defined application.
Agentic AI will upend this model. With many-to-many conversations between autonomous agents, these interactions can be continuous, context-aware, and highly variable, generating complex east–west data flows within campuses and across branches.
Consider an office environment: AI-powered video analytics monitor security and facilities. Cameras, sensors, and AI agents process foot traffic data locally and in the cloud. When an agent detects unusual crowding in a building, it triggers real-time coordination: security alerts, HVAC adjustments, or staff notifications. The result: an immediate spike in lateral campus traffic that is both unpredictable and essential.
Or from a branch perspective, a retail bank example: AI agents watch over physical security, customer flow, and IT health. An anomaly, for example numerous failed ATM transactions, prompts agents across branches to share logs, security feeds, and performance data. This agent-to-agent exchange crosses not just branches, but also cloud and data center links, in traffic bursts that defy legacy peak-planning models.
The end of over-provisioning for peak traffic?
Human-driven traffic tends to follow predictable daily rhythms: peaks during business hours and lulls overnight. Autonomous AI traffic behaves differently. Agent-to-agent “chatter” often runs continuously, establishing a persistent baseline of activity. On top of that, sudden and localized bursts can erupt when AI systems detect an event and immediately coordinate a response. And unlike traditional traffic patterns that center around fixed applications, these surges will appear in entirely different parts of the network from one moment to the next.
This unpredictability means the challenge will no longer simply be about how much traffic a network can carry overall. It’s about where, when, and how quickly capacity must shift to meet real-time demands.
A new approach to capacity planning
Typically, organizations provision for maximum anticipated load by relying on tools like load balancers or CDNs to smooth usage. In the AI edge era, that model will break down. Data sovereignty, privacy, and low-latency requirements will drive more local processing, a shift that will increase inter-site traffic and make east–west flows critical to maintaining performance and resilience.
Simply adding bandwidth everywhere won’t be practical nor cost-effective. Instead, networks must evolve to be elastic, dynamically reallocating resources as traffic patterns change. That means having application-aware networking capable of identifying and prioritizing agent-to-agent flows as they emerge, and using dynamic path selection to keep latency low while navigating unpredictable load shifts. It also means integrating security tightly with identity to guard against risks unique to autonomous systems, and investing in end-to-end visibility that goes beyond counting bytes to truly understand AI-driven traffic connectivity.
While we’re likely to see innovation and new solutions to alleviate some of the burden of always-on AI workloads, fact remains that capacity planning in the AI agent era will no longer be about provisioning for a peak, but about designing for intelligent, real-time adaptation.
Intelligence is the new peak capacity
Being AI-ready is not about scaling up bandwidth; it’s about making the network as adaptable and context-aware as the AI systems it supports. The next generation of campus and branch networks will be measured by how quickly they can recognize, prioritize, and secure AI-driven flows — anywhere, anytime, and in real time.
