Tomorrow's AI Capabilities Demand New Network Management
Agentic AI is changing networking. Autonomous, goal-driven software agents will soon create massive traffic spikes, straining today's brittle infrastructure and IP-based, NAT-heavy, and client–server networking model. As billions of AI agents come online, each creating frequent API calls and short-lived flows across multiple domains, we must rethink our network design. Graphiant provides a stateless, segment-routed core that scales, secures, and simplifies network operations for AI agents, large language models, and real-world scenarios. No more VPN tunnel sprawl or dependencies on overloaded PE routers, just performant, policy-driven connectivity built for AI agent networking.
The Coming Crisis: AI Agents and Network Scale
Network engineers already manage brittle stacks and manual processes. Add billions of AI agents, and the numbers stop working.
Explosion of connections: One public IP under NAT supports about 55,000 to 64,000 flows. If each agent drives around 10 flows, NAT tables bloat fast, grinding traffic to a halt.
Private, overlapping IPs: Most agents will run in private address spaces behind symmetric NAT, making peer-to-peer and multi-enterprise connectivity nearly impossible.
Ephemeral routing churn: AI workloads create short-lived agents that appear anywhere in the network, constantly advertising and withdrawing routes. Traditional VRF-based PE routers (e.g., Cisco ASR9K supporting ~10,000 VRFs and ~1.3M routes) cannot handle this scale and churn.
Security risks: Public Internet models require exposing API gateways and agent endpoints, increasing the attack surface. SSE vendors claim to protect this, but the real solution lies deeper—in the network fabric itself.
This is not theory. Real-world AI factories, network digital twins, and edge deployments show the pattern. More agents. More data sources. More API calls. More paths across different domains and providers.
Why Traditional Models Fail
Public Internet + NAT
- Designed for SaaS, not peer-to-peer AI agents.
- Symmetric NAT blocks direct agent-to-agent connections.
- NAT scale limitations create bottlenecks.
- Forcing every endpoint into public exposure is unsustainable.
IPSec VPN / SD-WAN
- Most SD-WAN vendors are just automating IPSec tunnels.
- Enterprises already struggle with 10,000+ tunnels for multi-cloud and branches. Adding AI agents increases tunnel counts exponentially (n² growth).
- Overlapping IPs between partners further breaks scaling.
MPLS VPN
- Excellent for fixed sites and predictable traffic, but not built for ephemeral agent churn.
- VRF limits and route leaking constraints make multi-enterprise collaboration nearly impossible at AI scale.
The Graphiant Approach
Graphiant was built to extend beyond SD-WAN into Agentic AI networking at scale:
- Stateless Core: No VRFs, no customer state in transit. Policy enforcement happens in a centralized control plane, not at every router.
- Transport-Mode IPSec: Millions of security associations scale naturally without tunnel explosion.
- Segment-Routed Core: MPLS-style stateless labels connect sites without provisioning complexity.
- Cloud-Based Control: BGP databases and route reflectors run elastically in the cloud, scaling dynamically with agent churn.
- B2B and Peer-to-Peer Support: Agents in multiple enterprises can connect securely without NAT hacks or overlapping IP headaches.
Why It Matters for Agentic AI
AI agents, custom agents, and LLM-driven services depend on predictable, policy-driven connectivity.
- Resilient Scale: Support millions of ephemeral agents and flows. Maintain throughput as models, tools, and services expand.
- Security by Design: Keep endpoints off the public Internet. Enforce policy at the network layer. Meet compliance needs without fragile per-site rules.
- Policy-Driven Connectivity: Define which agents, users, or services can talk. Use network data to respond to potential issues in real time.
- Future-Proof: Works for large language models, generative AI, and machine learning across clouds and edge. Fits real-world deployment patterns and training or inference pipelines.
How Teams Use It
- Engineering managers and network engineers. Replace manual processes with intent and automation. Shorten deployment times. Reduce error-prone change windows.
- Product managers and AI platform owners. Connect agents to other agents, data sources, and services across clouds and partners. Ship features faster with a stable network layer.
- Security and compliance. Apply consistent policy, logging, and segmentation across multiple domains. Prove control with clear network data and insights.
- Operations. Give the human operator the tools to diagnose, test, and fix issues fast. Use open standards to integrate existing systems and protect prior investments.
Real-World Scenarios to Consider
- AI factories and agent swarms. Thousands of agents use natural language to query a knowledge base, call APIs, and act on data. The fabric must route, secure, and account for each step.
- B2B data exchange. Different enterprises, overlapping IPs, and strict compliance requirements. Graphiant enables direct, policy-approved paths without Internet exposure.
- Network digital twins. Model network changes, test code and policy, then push safely. Use the same stateless constructs in staging and production.
- Edge and branch. Small footprints with big AI demands. Keep the model simple and the performance high.
A New Path Forward
Networking is the hidden bottleneck of the AI revolution. NAT tables, VPN tunnels, and VRF limits were never designed for billions of autonomous agents. Agentic AI requires a new architecture that's stateless, scalable, and policy-driven.
Graphiant delivers exactly that. By eliminating tunnel sprawl, decoupling control from the data plane, and enabling true multi-enterprise agent connectivity, Graphiant ensures the network will not only survive the agentic AI era but empower it.
