Your infrastructure already has the raw materials: compute (VMs, containers, serverless), event streaming (Kafka, Kinesis, Pub/Sub, RabbitMQ), data stores (warehouses, databases, object storage), and AI endpoints (any hosted or self-hosted LLM).

Those are excellent raw materials. But to make them agent-ready — to turn them into a Context Mesh that your AI agents can actually rely on — you need intelligent connective tissue between them.

That's where Kong comes in.

Kong acts as a single, unified control plane across the three types of traffic your agents depend on:

• - **API traffic** — requests from agents to backend services and data stores
• - **Event traffic** — real-time streams from Kafka, Kinesis, Pub/Sub, and other event infrastructure
• - **AI traffic** — prompts and responses flowing between agents and LLMs

Most organizations try to govern these three separately, with three different tools, three different policy models, three different teams. That's how you end up with a fragmented mess that's impossible to audit and expensive to maintain.

Kong governs all three from one place — regardless of where that place is.

Let's break down how this works layer by layer, looking at the AI Gateway, Event Gateway, and API Gateway.

Every prompt your agent sends, every response it receives — it flows through Kong's AI Gateway before it reaches your LLM. That's true whether you're routing to OpenAI, Anthropic, a hosted model on Vertex AI or Bedrock, or a self-hosted model running on your own inference infrastructure.

This isn't middleware for middleware's sake. The AI Gateway does things your LLM provider can't:

**PII masking before prompts leave your perimeter.** If an agent is reasoning over customer data, the raw PII gets masked before the prompt hits the model. The model sees anonymized context. The original data never leaves your environment — not to a cloud API, not to any external endpoint.

**Confidence threshold enforcement.** If the model returns a low-confidence response below a threshold you set, Kong can route to a fallback model, trigger a human review workflow, or simply block the action. You decide what acceptable looks like — and that policy travels with you across providers and deployments.

**Semantic caching.** Agents ask similar questions repeatedly. Kong recognizes semantically equivalent requests and serves cached responses — reducing latency, cutting LLM costs, and reducing load on your inference endpoints regardless of which model or provider serves them.

**Prompt injection detection.** Adversarial inputs designed to manipulate agent behavior get caught at the gateway before they reach the model.

**Cost attribution and metering.** Every token consumed, by every agent, attributed to the right team or workflow. When AI costs become significant at scale — and they will — you need this data. Especially if you're running across multiple providers or model tiers.

Real-time context delivery is what separates a Context Mesh from a fancy API layer.

Kong's Event Gateway pipes your event streams into the agent's context window safely and at speed. Whether your streaming infrastructure is Kafka (self-managed or cloud-managed), Kinesis, Pub/Sub, or another broker — agents subscribe to the streams they need. When state changes — inventory updates, customer actions, transaction events, infrastructure alerts — the agent knows immediately.

The Event Gateway enforces the same governance model as everything else: access controls, rate limiting, observability. An agent can't subscribe to an event stream it doesn't have permission to read. And every subscription, every event consumed, is logged.

This is the piece that eliminates stale data hallucinations. Your agents stop reasoning from batch snapshots. They reason from the current state of your systems. That's true whether those systems are running in a public cloud or in a datacenter three floors below you.

Your existing backend services — the microservices, the databases, the legacy systems — they don't go away. Agents need to take actions against them: create records, trigger workflows, retrieve specific data.

Kong's API Gateway secures all of that. Zero-trust policies mean agents only call what they're allowed to call. Rate limiting prevents a runaway agent from flooding downstream services. The transformation layer translates API responses into formats that are actually useful for LLM reasoning — instead of forcing the model to parse raw JSON designed for a mobile app.

This layer works identically whether your backend services run on Kubernetes in a public cloud, on bare metal in your own datacenter, or as a mix of both.

Think of Kong as implementing the Backend for Agents pattern across all three traffic types simultaneously.

You're probably familiar with Backend for Frontend (BFF): instead of letting your mobile app call a dozen raw microservices, you put an aggregation layer in front that gives the app exactly what it needs. Cleaner API, better performance, single place for security logic.

The BFA does the same thing for AI. You don't want your agents calling raw microservices. The security model is wrong — agents can't be trusted to enforce their own access controls. The data model is wrong — microservices weren't designed to produce context for LLM reasoning. And the governance model is wrong — there's no single place to audit what your agents accessed, when, and why.

Kong provides that single place. All agent traffic — to APIs, to event streams, to AI models — flows through Kong. One policy model. One audit log. One place for your security team to understand what your AI is doing.

The BFA pattern is infrastructure-agnostic by design. The discipline is the same whether you deploy on EKS, AKS, GKE, OpenShift, or a private Kubernetes cluster. What matters is that there's an intermediary between your agents and everything they touch — and that intermediary enforces consistent policy.

Here's a practical first phase that works on any Kubernetes-based environment, cloud or on-premises:

• - **Week 1–2: Deploy Kong Konnect as your control plane**
  Stand up Kong on your Kubernetes cluster of choice. Configure your first API Gateway route to an existing internal service. Verify zero-trust policy enforcement is working. Add your first plugin (rate limiting, authentication — your call). This is your foundation. Everything else builds on top of it.
• - **Week 2–3: Connect your first AI workflow**
  Pick one agent workflow and route it through Kong's AI Gateway. Enable PII masking for any workflow touching customer data. Set up token metering. It doesn't matter which LLM is on the other end. You now have governed AI traffic — and a model for how every subsequent workflow should be onboarded.
• - **Week 3–4: Add your first real-time context source**
  Connect one event stream through Kong's Event Gateway. Wire it to your agent's context. Run a comparison: agent performance with batch data vs. agent performance with real-time events. This is usually where the value becomes undeniable — and where the rest of the organization starts paying attention.

By the end of 30 days, you have a working Context Mesh foundation: governed API traffic, governed AI traffic, and real-time event delivery. You haven't replaced your existing infrastructure. You've made it agent-ready.

Not everything runs in a public cloud. Regulated industries — financial services, healthcare, defense, government — often can't send data to cloud APIs at all. That's not a blocker for the Context Mesh pattern. It's where it becomes more critical, not less.

Kong deploys on any Kubernetes distribution, including air-gapped environments. Your LLMs can run on self-hosted inference infrastructure. Your event streaming can run on on-premises Kafka. Your data stays where your compliance requirements say it has to stay.

The governance model doesn't change. PII masking, access controls, audit logging, cost metering — all of it works identically on-premises as in the cloud. The control plane (Kong Konnect) can be self-hosted too for fully air-gapped deployments.

What you give up by staying on-premises is managed infrastructure and elasticity. What you don't give up is the architecture pattern, the governance model, or the ability to run reliable agentic AI at scale.

Before you scale, answer these questions for yourself:

1. - **Who owns the data path?** As agents multiply, the data path — from agent to LLM, from agent to MCP, from agent to API, from agent to event stream — becomes the most critical piece of your AI infrastructure. Someone needs to own it. Not as an afterthought.
2. - **Who owns cost and risk across the entire path?** AI token costs, event streaming costs, API call costs — they all compound as you scale. Without centralized visibility, cost attribution becomes impossible and runaway agents become expensive. This problem is worse in multi-cloud or hybrid environments, not easier.
3. - **What's your audit story?** When something goes wrong — and something always goes wrong — can you reconstruct exactly what data your agent had access to, what it did with it, and what decision it made? Regulators are already asking this question. It doesn't matter which cloud the data passed through.
4. - **What happens when you switch providers?** If your governance model is baked into a single cloud's native tooling, you've locked yourself in at the worst possible layer — the one that sits between your agents and everything else. A cloud-agnostic control plane means provider decisions don't become architecture decisions.

Kong makes these questions answerable. That's not a feature. That's a requirement for running AI at enterprise scale.

The enterprises that will lead in agentic AI over the next three years aren't the ones making the biggest bet on a single model or a single cloud. They're the ones building infrastructure that makes any model, any cloud, and any deployment topology work reliably at scale.

The Context Mesh is that infrastructure. Kong gives you the control plane to build it today — on whatever foundation you're already running.

**Ready to build your Context Mesh? **[Talk to a Kong solutions engineer](https://konghq.com/company/contact-us)Talk to a Kong solutions engineer about your specific architecture and deployment environment.[](https://docs.google.com/document/d/1IsoR9q5GCuxcBvssEhROVWWA-5vuks__q4Bb8mmdwJ4/edit#)