AI is moving at lightning speed. What used to be a world of static models generating text has become something much bigger. Large Language Models (LLMs) are turning into agents — systems that can reason, plan, and take action on behalf of humans. At the center of this shift is the Model Context Protocol (MCP).
MCP is like a common language for AI systems. It allows them to connect with external tools, APIs, and data sources without custom hacks. For enterprises, that’s a huge deal. It means they can finally build agentic AI infrastructure that’s flexible, scalable, and connected.
But here’s the challenge: adopting MCP in the real world isn’t simple. Enterprises have to think about MCP server security, AI agent governance, and enterprise AI compliance. Without those, even the most promising AI projects can stall.
That’s where Kong comes in. With years of experience in API management, Kong is extending its ability to AI with the Kong AI Gateway MCP integration. It acts as a universal control layer for MCP, making sure organizations can embrace agentic AI safely and at scale.
Kong AI Gateway: A Control Plane for MCP
Think of the Kong AI Gateway as the “traffic cop” for MCP communications. Every request, every tool call, every interaction flows through it. Here’s what that unlocks:
- Secure exposure: Enterprises can safely expose MCP servers to AI clients while enforcing strict security policies.
- Dynamic routing: Tool calls get directed to the right backend with load balancing and failover built in.
- Protocol translation: Requests from MCP don’t get stuck if a service speaks REST, gRPC, or GraphQL. Kong makes them compatible.
Built-in MCP Server: Making Life Easier
Kong doesn’t just manage MCP traffic. It also ships with its own MCP server inside Kong Konnect. That means enterprises don’t need to start from scratch.
Some highlights:
- Natural language queries: Developers and AI assistants can ask questions about gateway configurations, services, or analytics — in plain English.
- Real-time analytics: AI agents can access traffic data, error rates, and performance insights instantly.
- Infrastructure discovery: LLMs can explore Kong services and plugins, making it easier to design agent workflows.
For teams, this is a game-changer. It reduces the complexity of experimentation and makes it easier to move from proof of concept to production.
Compliance and Governance: Guardrails for Enterprise AI
It’s one thing to get MCP running. It’s another to make sure it’s safe. Enterprises need to be sure that AI agents aren’t breaking compliance rules, leaking sensitive data, or racking up huge costs.
Kong bakes governance into the AI Gateway so organizations don’t have to bolt it on later. Key features include:
- Authentication and authorization: Only verified AI clients get access, with options like OpenID Connect, JWT, and ACLs.
- Rate limiting: Requests and token usage can be capped to control costs and prevent abuse.
- Guardrails for safety: Prompt security, content moderation, and PII sanitization all work to keep AI interactions safe.
That’s the foundation of AI agent governance. Enterprises can push forward with innovation, knowing they won’t be blindsided by compliance risks.
Current MCP and AI Gateway Capabilities
Kong is already delivering real features that enterprises can use today.
MCP Server Support
There are two primary ways to run MCP with Kong:
- External MCP servers: If you’ve already built or customized MCP servers, Kong AI Gateway can expose them securely, with policies wrapped around them for authentication, rate limiting, and compliance.
- Kong Konnect MCP server: For teams that want something ready-made, Kong ships with its own built-in MCP server. That makes it easy to query configurations, get analytics, and explore services without spinning up extra infrastructure.
AI Gateway Features for MCP Traffic
Kong has extended its gateway plugins to handle MCP-specific needs. Some highlights include:
- AI Proxy Plugin: A single interface to interact with multiple LLM providers and MCP servers
- AI Rate Limiting Advanced: Helps manage token consumption and keep usage costs predictable
- AI Prompt Guard: Blocks prompt injection attacks with regex or semantic policies
- RAG Injector: Injects retrieval-augmented generation (RAG) data into prompts automatically
- Semantic Caching: Recognizes similar prompts to cut down redundant calls and improve performance
Observability and Analytics
Visibility is everything when running MCP in production. Kong makes this simple with:
- Pre-built dashboards to track usage and token consumption
- Distributed tracing with OpenTelemetry so you can follow tool calls across services
- Log exports to SIEM systems to meet compliance and auditing needs
Hybrid and Multi-Cloud Deployment
Flexibility is non-negotiable for enterprises. Kong AI Gateway supports:
- Konnect SaaS for those who want a managed experience
- Hybrid mode for organizations running across data centers and clouds
- Kubernetes integration with Kong Ingress Controller for cloud-native teams
The Road Ahead: LLM Orchestration and Smarter Workflows
Kong’s work with MCP isn’t stopping at what’s already available today. The roadmap shows a strong commitment to giving enterprises more flexibility, stronger security, and richer AI workflows. Here’s what’s likely coming in the future:
Enhanced MCP Tool Discovery
· Unified service catalog: Enterprises will be able to publish MCP servers into Kong Konnect’s catalog, so they’re discoverable internally or even externally if needed.
· Developer portals: Onboarding AI developers will get easier, thanks to curated portals with documentation, access controls, and clear guidance.
Advanced Agentic Workflows
- Prompt chaining: Instead of stopping at a single tool call, workflows will be able to chain multiple prompts together for more complex outcomes.
- Stateful sessions: Long-running agentic interactions will be possible, with context persisting across sessions.
Expanded Protocol Support
- Agent2Agent (A2A) communications: Beyond MCP, Kong is planning support for agent-to-agent protocols to handle richer multi-agent ecosystems.
- gRPC and GraphQL adaptors: Native support for these API styles will make MCP tool definitions more versatile.
Enterprise Security Enhancements
- mTLS for MCP servers: End-to-end encryption will be a default, not an afterthought.
- Fine-grained RBAC: Role-based controls will let enterprises manage exactly which tools and resources agents can access.
Ecosystem Integration
- Multi-LLM orchestration: Enterprises won’t have to lock into a single LLM. Deeper integrations with OpenAI, Claude, Mistral, and others will allow for mixing and matching.
- Third-party MCP servers: Kong AI Gateway will be optimized and certified for popular external MCP servers, making it easier to bring outside capabilities into enterprise workflows.
Why Kong’s Approach Matters for Agentic AI Infrastructure
Enterprises don’t adopt new tech just because it’s shiny. They adopt it when it’s safe, consistent, and developer-friendly. Kong hits all three.
- Enterprise readiness: Zero-trust security, encryption everywhere, and compliance support for regulations like GDPR and HIPAA.
- Operational consistency: A unified platform where MCP tools and traditional APIs are managed side by side. That’s the power of decades of API management
- Developer empowerment: Self-service onboarding, GitOps workflows, and no-code policy management give teams the freedom to innovate without red tape.
This balance between safety and speed is what makes Kong’s approach so valuable. It brings agentic AI infrastructure out of the lab and into the enterprise.
Orchestrating the Future of Enterprise AI
The future of AI in business won’t be about isolated models or experiments. It’ll be about orchestrated, connected, agent-driven systems that can operate safely at scale.
Kong is making that future possible today. With Kong AI Gateway MCP integration, organizations can adopt the Model Context Protocol enterprise without sacrificing MCP server security, AI agent governance, or enterprise AI compliance. Add in world-class AI observability and flexible deployments, and you’ve got a platform that’s both powerful and trustworthy.
The bottom line? If you’re building your AI strategy for the next decade, you’ll need more than models and prompts. You’ll need governance, observability, and a solid infrastructure foundation. That’s exactly what Kong delivers.
The future of AI isn’t just possible. It’s secure, scalable, and already within reach.
