AI is moving from chat to action and governance needs to catch up
For most of the last two years, “enterprise AI” meant employees talking to a chatbot. That era is evolving, and fast. AI is now taking action — agents are calling into backend systems, querying databases, executing workflows, and moving money and data on behalf of the business.
That shift will be the story of 2027. And it is creating a governance gap that most enterprises haven’t yet named, let alone closed.
The traffic your organization needs to govern is no longer just users talking to a large language model (LLM). It’s now two distinct layers:
- The LLM layer — prompts and responses flowing to and from model providers.
- The agentic layer — agents calling Model Context Protocol (MCP) servers to actually do things inside your environment.
It’s worth being precise about what that second layer really is, as it can be misunderstood. An MCP server is essentially a standardized, self-describing API. It advertises to an agent exactly what it can do and how to call it. And critically, those capabilities aren’t just read-only; many are writable, meaning an agent can create, change, and delete data, not just retrieve it. That’s what makes MCP so powerful: it puts your systems and data within easy reach of any agent, in a standard approach. But that same accessibility is exactly what makes it dangerous when it isn’t governed.
Most enterprises are planning or actively addressing governance and visibility into the first layer. Yet almost none have put the same focus on the second. That’s the problem — and it’s compounding by the week.
The “MCP sprawl” problem no one is talking about yet
If you’ve lived through shadow IT, shadow SaaS, or shadow API sprawl, you already know how this movie ends. MCP is on the same trajectory, only faster.
Every team spinning up an agent is standing up MCP servers to give it hands and feet. Each server comes with its own endpoint, its own authentication scheme, its own rate limits (or none), and its own blind spot in your observability stack. Multiply that across a large enterprise and you have an ungoverned traffic layer sitting between your AI agents and your most sensitive systems — with no consistent identity, no unified audit trail, and no way to say “this agent can talk to that server, but not this one.”
And this isn’t hypothetical. The industry has already seen cases where an agent given access to production systems without adequate controls deleted critical data in seconds. When agents can reach your systems directly and nothing structural stands in the way, a single bad decision becomes irreversible.
For regulated industries, such as financial services, healthcare, and public sector, that’s not a future risk. It’s a present-day audit and compliance exposure.
The point-product trap makes it worse, not better
The instinct in most organizations is to solve this the way we solved every previous wave: buy a point tool for each new problem.
An LLM gateway for prompt routing. A separate MCP gateway for agent traffic. Another tool for token-level cost tracking. Another for guardrails. Another for observability. Another for identity offload. Each one is a proxy, each one is a hop, each one has its own console, its own contract, and its own bill.
The result is exactly the sprawl you were trying to prevent, just moved up a layer. And there’s a hidden performance tax underneath it: AI traffic is volumetric. LLM and agent workloads generate enormous packet counts and payload sizes, and every additional proxy hop means another decrypt, another inspection, another re-encrypt. Latency compounds precisely where AI is most sensitive to it, and cost compounds precisely where CFOs are already asking hard questions.
Executives should be wary of any AI governance strategy whose architecture diagram looks like a chain of five vendor logos.
What “governed AI” actually needs to look like
To get in front of this, enterprises need a governance model with three properties:
- Both sides of AI traffic, one control point. LLM traffic and agent/MCP traffic managed from the same platform, with the same identity, the same policy engine, and the same audit trail.
- Governance on infrastructure you already own. No new procurement cycle, no new operational silo, no new contract to renew every time AI evolves.
- Architecture that doesn’t punish you for scaling. A single-pass data path — one decrypt, one inspection — instead of a chain of proxies that multiplies latency and cost as AI traffic grows.
That’s the bar. And it’s the bar we built to.
How NetScaler closes the gap across both LLM and MCP workloads
With the addition of MCP Gateway capabilities to Citrix NetScaler AI Gateway, NetScaler now governs both layers of enterprise AI traffic from a single platform:
- Citrix NetScaler AI Gateway governs the LLM layer. Provider-agnostic routing across Vertex AI, Bedrock, and Azure; centralized authentication; and token-level usage and cost visibility by team, user, and application.
- MCP Gateway capabilities governs the agentic layer. A single entry point in front of your backend MCP servers, with per-user and global token auth, OAuth-based access, allow/block listing to keep agents on approved servers, tool-level rate limiting to control cost and abuse, and session persistence so multi-step agent workflows don’t break mid-operation.
One platform. One dashboard. One policy model. Both sides of enterprise AI, the traffic going to models and the traffic going to your systems, governed together, not stitched together.
And because it all runs on NetScaler’s single-pass architecture, that governance doesn’t come at the expense of the performance AI workloads demand. Every task in the data path happens in one pass — one decrypt, one inspection point — instead of chaining through separate proxies and paying the latency and cost penalty on every hop.
The cost conversation executives will actually want to hear
Here’s the part that changes the ROI conversation.
MCP Gateway and the AI Gateway enhancements are included in existing NetScaler entitlement at no additional cost. There is no separate SKU, no add-on license, no metered fee for governing your MCP or LLM traffic.
And for enterprise customers, NetScaler itself is included in Citrix enterprise licensing at no additional cost. The platform governing both sides of your AI traffic is already in your environment, already in your contract, and already in your operational muscle memory.
That means the answer to “what do we need to buy to get AI governance under control?” is, for a large share of Citrix customers, nothing new. You turn it on. You point your LLM and MCP traffic at it. You get one control point, one dashboard, one audit trail — across the entire AI stack.
The executive takeaway
Agentic AI is going to be the defining infrastructure conversation of the next 24 months. The organizations that get ahead of it will be the ones that treat governance as an architectural decision, not a shopping list of point products.
NetScaler was built for exactly this moment: to be the connective tissue that governs how AI traffic moves end to end, on infrastructure you already own, without a new line item on the budget.
The AI governance gap is real. The good news is, for most of you, closing it doesn’t require a new vendor — it requires turning on what’s already there.
