MCP servers expose all tools by default. There are two problems with this: security (agents get capabilities they shouldn't have) and performance (too many tools degrade LLM tool selection). The solution? Put a gateway between agents and MCP servers that filters tools based on who's asking. Default…
The Kong MCP Registry acts as a central directory for AI agents and clients to access services that provide context or take action. For AI agents, think of it as a combination of a "Service Catalog" and a "Developer Portal." It offers the metadata, API endpoints, and capability definitions that…
AI spending is exploding across the organization—but often not in the ways leadership approved or finance can track. Development teams spin up LLM connections to ship features faster. Data teams provision GPU clusters for experiments that get abandoned. Multiple teams solve the same problem three…
Let's be honest about what's happening inside most enterprises right now. Development teams are under intense pressure to ship AI features. The mandate from leadership is clear: move fast. And so they do. They spin up LLM connections, integrate third-party AI tools, and route data to models without…
The example below shows how an AI agent can be built using Volcano SDK with minimal code, while still interacting with backend services in a controlled way. The agent is created by first configuring an LLM, then defining an MCP (Model Context Protocol) endpoint that represents a promotions service.…
AI agents are spreading across organizations rapidly. Each agent needs secure access to different Model Context Protocol (MCP) servers. Authentication becomes complex. Scaling creates bottlenecks. The dreaded "too many endpoints" problem emerges. You face a classic AI infrastructure headache. The…
If you've used KAi before, you'll notice the difference immediately. The enhanced version has deeper access to your Konnect environment and new tools for debugging, analytics, and configuration management: The new KAi is more capable because it's backed by a structured set of tools that can query…
The report identifies a mindset trap that's holding most organizations back: "inside-out" integration thinking. Inside-out means viewing integration from the perspective of only prioritizing the reuse of legacy integrations and architecture (i.e., simply wrapping existing integrations with MCP and…
The first pillar is enablement. Developers need tools that reduce friction when building AI-powered applications and agents. This means providing: The goal isn't to replace existing development workflows but to extend them. Developers should be able to build agents using familiar paradigms while…
The evolution of AI agents and autonomous systems has created new challenges for enterprise organizations. While securing API endpoints is well-understood, controlling access to individual AI agent tools presents a unique authorization problem. Today, we're excited to announce a powerful solution…
In one of our posts, Kong AI/MCP Gateway and Kong MCP Server technical breakdown, we described the new capabilities added to Kong AI Gateway to support MCP (Model Context Protocol). The post focused exclusively on consuming MCP server and MCP tools through Kong MCP Gateway. Now, it's time to check…
Every January, the same resolutions show up: eat better, exercise more, finally learn that language, finally figure out that production use case for AI agents (OK, this one isn’t so typical unless you operate in our universe). But if you're responsible for your organization's AI strategy, we'd like…
A decade ago, we set out to connect the world through APIs, which we saw as fundamental building blocks of software. Before Kong, we founded Mashape as the first API marketplace to provide an assembly line for developers building apps, and then we open-sourced its core API runtime, Kong, the…
We know that successful AI connectivity programs often start with an intense focus on how you govern and protect LLM and MCP traffic. This is why Kong was first to market with an enterprise-grade AI gateway , and it’s why, in our last release, we added enterprise MCP proxy support to our…
Companies are charging headfirst into AI, with research around agentic AI in the enterprise finding as many as 9 out of 10 organizations are actively working to adopt AI agents. LLMs are being deployed, agentic workflows are getting created left and right, and the promises of what this technology…
A close examination of what really powers the AI prompt unveils two technologies: the large language models (LLMs) that empower agents with intelligence and the ecosystem of MCP tools to deliver capabilities to the agents. While LLMs make your agents smart, it would be hard to provide agents with…
Secure your AI infrastructure with prompt guards, PII sanitization, and centralized governance. Control LLM costs with token-based rate limiting and semantic routing across providers.