Gartner's strategic planning assumption stops you in your tracks: by 2029, 50% of software application providers will be forced to share their context layer externally for third-party orchestrators to stay relevant, versus less than 2% today. Let that number sink in. Nearly every enterprise…
Agents are ultimately decision makers. They make those decisions by combining intelligence with context, ultimately meaning they are only ever as useful as the context they can access. An agent that can't check inventory levels, look up customer history, or verify pricing isn't autonomous — it's…
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…
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.…
MCP is an open standard that defines how AI clients communicate with remote servers. It provides a standardized protocol for clients like Claude, Cursor, or VS Code to access tools, resources, and capabilities from external systems. Currently, MCP servers can expose several types of capabilities to…
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…
Kong Insomnia 12.3 TL;DR? Your New Year’s resolution is probably something along the lines of “work smarter, not harder.” Our resolution is to make that actually possible. Insomnia 12.1 and 12.2 sharpened your tools, and now Insomnia 12.3 removes the friction to allow you to focus on building great…
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…
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…
In the latest Kong Gateway 3.12 release , announced October 2025, specific MCP capabilities have been released: The Kong AI Gateway sits in between the AI applications we build and the MCP Servers and GenAI models we consume. In fact, the Kong MCP Gateway sits side-by-side with the Kong LLM…
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…
Data might be the fuel for AI. But APIs are the proper way to package that “fuel” as AI-ready “food” is through the API. AI coding tools can do a lot without APIs, but at some point they’ll need to be able to: . . . and all ideally without the engineer having to…
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.