Modern applications are evolving quickly. AI-powered components embedded directly into the user experience complicate what used to be a predictable flow of web and API traffic. Search features that understand intent, assistants that automate workflows, recommendation engines that tailor content, and agents that can act on behalf of a user have driven both bespoke application experiences for users and complexity in traffic management for the teams who support these applications.
These capabilities are rapidly becoming standard features in mission-critical applications. And while AI may still feel like a separate domain, AI functionality predominantly runs through the same infrastructure that delivers applications today. This confluence of traffic types, accompanying such an evolution of application capabilities, requires an application delivery and security strategy that can support AI-augmented application experiences just as reliably as traditional application traffic.
A key part of enabling that shift is Model Context Protocol (MCP), a new standard for connecting AI assistants to the tools and data of the enterprise, including content repositories, business tools, and development environments. This makes MCP foundational to providing efficient application delivery for AI workloads.
“MCP will be foundational to modern application delivery. It bridges AI agents and enterprise systems, enabling organizations to operationalize AI features safely and at scale without introducing silos.”
Why MCP matters for today’s applications
Large language models (LLMs) illustrate the challenge. While they can transform user experiences, they lack inherent capabilities to interact with external systems that hold business logic, user data, or workflows. MCP addresses this by providing a structured, secure, vendor-neutral way for AI agents to discover tools, request actions, exchange information, and orchestrate multi-step workflows, all within clear permissions and boundaries. This positions MCP to function as an interoperability layer that provides a consistent way for AI systems to "plug into" the enterprise.
This interoperability is essential because the value that an AI model can bring rarely comes from the model alone. To fully enable this interoperability, a model must integrate with larger business contexts securely, predictably, and in real time. MCP standardizes those integrations and makes them repeatable across environments.
How MCP enables secure AI workloads
When people think of AI in the context of application delivery, they may think of data delivery, model training, or S3-based pipelines. Those are important use cases that application delivery teams will need to solve, and F5 has introduced capabilities tailored specifically for them. But MCP’s role in application delivery is equally impactful and more immediate to the end user experience.
Because AI workloads generate new kinds of traffic patterns and new operational requirements, their interactions differ from traditional web, application, or API requests in three critical areas:
- Sensitivity to latency: Many AI workflows rely on multiple MCP calls to assemble context or orchestrate actions. The performance of these calls directly affects the user experience. Application delivery infrastructure is uniquely positioned to boost this area of performance, optimizing latency, priority, routing, concurrency, and tool-specific traffic patterns. This ensures AI features behave as responsively and reliably as any other application component.
- Security governance: MCP introduces a new governance layer for enterprise applications. By enabling AI agents to invoke tools, access data, and execute workflows, MCP demands the same rigor traditionally applied to APIs. Native MCP support allows application delivery solutions to enforce identity-based controls, inspect and log traffic, apply rate limiting with anomaly detection, and prevent misuse or over-permissioned tools. These capabilities ensure MCP interactions are governed with the same security and consistency as existing service-to-service traffic.
- Operational resilience: As AI features become integral to user experiences, MCP endpoints must be treated as mission-critical services. Delivery-layer support enables intelligent load balancing, health checks tailored to MCP exchanges, failover planning, and multi-region availability. This prevents MCP endpoints from becoming single points of failure and ensures consistent service delivery.
F5 BIG-IP v21.0: Native MCP support for AI workloads
F5 BIG-IP v21.0 expands the core of F5 BIG-IP Local Traffic Manager capabilities to manage MCP traffic patterns originating from application workflows.
Organizations can route and optimize MCP-based calls with the same precision as HTTP or TCP requests, apply custom business logic through F5 iRules, enforce consistent access and security policies, and scale MCP endpoints as part of a unified application delivery strategy. This approach enables enterprises to support AI-enhanced and traditional workloads on a single, proven platform, eliminating the need for parallel infrastructures and positioning MCP as a core component of modern application delivery.
Future trends: Why MCP will be foundational
As AI matures, several trends will accelerate MCP’s importance:
- AI features will become standard in all applications, making intelligent interactions as common as API calls.
- Policies will evolve to become context-aware, considering what the AI agent intends to do.
- Operational consistency will matter more than ever, as even minor latency spikes or routing variations can degrade AI-driven experiences.
MCP will be foundational to modern application delivery. It bridges AI agents and enterprise systems, enabling organizations to operationalize AI features safely and at scale without introducing silos. For organizations planning to expand AI capabilities, or simply preparing for what’s next, native MCP support in the application delivery layer is essential for future-proofing a high-performance, AI-driven application delivery strategy.
Contact us to learn more about how F5 BIG-IP can help your applications meet the AI-driven moment.
About the Author
Griff Shelley is a Product Marketing Manager at F5, specializing in hardware, software, and SaaS application delivery solutions. With a passion for connecting innovative technology to customer success, Griff drives go-to-market projects in global and local app delivery, cloud services, and AI data traffic infrastructure. Prior to his career in tech, he was a post-secondary education academic advisor and earned degrees from Eastern Washington University and Auburn University.
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