Making AI Infrastructure Operable at Scale with Native MCP Support
As GPU clusters grow into the tens of thousands, infrastructure teams face a perfect storm:
- Complex east-west topologies
- Multi-NOS environments
- Pressure to deliver lossless performance
- A shortage of specialized skills
Manual processes and fragmented tools slow deployments, stretch troubleshooting into hours, and leave expensive GPUs underutilized. The result is higher operational cost, delayed time-to-value, and limited ability to scale confidently.
Three major challenges stand out today:
- Operational complexity that refuses to scale: Designing, validating, and running high-performance AI fabrics with complex topologies, RoCE, and mixed SONiC and Cumulus environments still requires heavy human effort and expert knowledge at every stage.
- Fragmented visibility across the stack: Without a unified view of network, compute, and storage, operators struggle to quickly find and fix problems like congestion or misconfigurations that quietly slow down AI workloads and waste expensive GPU capacity.
- The gap between traditional automation and true agentic operations: Most platforms don’t give AI agents a secure and standardized way to work with the live network and its data while keeping operations under control. This leaves infrastructure teams stuck between fragile, hard-to-maintain scripts and high-risk experimentation on production environments.
How Verity 6.6.1 with MCP Solves These Challenges
Verity 6.6.1 combines intent-based orchestration, full-stack observability, and native MCP support to tackle each of these challenges directly.
- Complexity that scales with intent, not headcount:
Our intent-based model lets operators define the desired fabric once, including topology, roles, policies, QoS, and multi-fabric isolation. The platform then builds and maintains it across SONiC and Cumulus. Built-in support for multiple Spectrum-X reference architectures, spine planes, rail-optimized designs, GPU cabling validation, and east-west NIC management removes the manual toil of Day 0 design and Day 2 changes. The result is faster, error-free deployments and consistent operations even as clusters grow. - Full-stack visibility instead of silos:
Our observability & AIOps suite provides the only unified layer spanning network, compute, and storage. Operators gain a single, correlated view of fabric health, congestion, and performance. This is exactly the context AI agents and infrastructure teams need to move from reactive firefighting to proactive optimization. When every minute of GPU downtime carries real cost, this visibility turns guesswork into actionable insight. - Secure agentic operations, not experiments:
Native MCP support turns our solutions suite into a standardized, policy-aware interface for LLMs and multi-agent systems. Agents are able to securely discover and act on live intent, topology, telemetry, and policies without custom code or unconstrained access. Role-based controls and human-in-the-loop options keep operations safe while unlocking natural-language workflows for troubleshooting, compliance, predictive maintenance, and remediation. This is the practical bridge from today’s assisted automation to tomorrow’s autonomous AI infrastructure.
Built for Today, Ready for Tomorrow
Release 6.6.1 doesn’t just add another feature. It removes the structural barriers that have kept AI infrastructure operations complex, opaque, and heavily manual. By combining production-grade intent-based orchestration, full-stack observability, and industry-first MCP support, BE now gives operators the foundation to run high-performance fabrics at scale.
Andrew Froehlich
Chief Marketing Officer
Andrew Froehlich has more than 25 years of experience in enterprise technology. He has served as a marketing director for several tech startups and provided marketing consulting to various companies. Combining deep technical knowledge with strong expertise in content strategy and messaging, he currently serves as Chief Marketing Officer at BE.