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.…
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…
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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…
APIs have quietly powered the global shift to an interconnected economy. They’ve served as the data exchange highways behind the seamless experiences we now take for granted — booking a ride, paying a vendor, sending a message, syncing financial records, or stitching together SaaS tools into a…
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…
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…
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 enterprise AI landscape is transforming much faster than most organizations can adapt. One moment you're testing ChatGPT for customer service. The next, you're orchestrating dozens of AI agents that need secure access to databases, APIs, and multiple LLM providers. This rapid evolution creates…
APIs are the connective tissue of digital products and services, and they're the lifeblood of AI. APIs shape customer experiences, power partner ecosystems, and accelerate enterprise innovation. As organizations double down on API-first strategies, one role is emerging as indispensable: the API…
API endpoints are like the doors to a web service. Through these endpoints, we can enter and talk to a web service and be shown where and how we can gain access to whatever it is the server has. This process allows separate pieces of software to swap information in a controlled way. Understanding…
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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…
AI governance establishes the principles, roles, processes, and controls for responsible AI deployment. It transforms abstract ethics into concrete practices. Think of AI governance as a rulebook for how to use AI in a secure, ethical, observable, moderated, and auditable fashion. It’s a…
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…
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.