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…
The solution landscape is complicated by the fact that MCP is still finding its footing, and there are many various OSS projects and vendors that are rapidly shipping “MCP support” in an attempt to take advantage of the hype and attention around MCP. Unfortunately, many early attempts at solutions…
The hype for Anthropic’s Model Context Protocol (MCP) has reached a boiling point. Everyone ( including Kong ) is releasing something around MCP to ensure they aren't seen as falling behind in the ever-changing AI landscape. However, in this mad dash, there remains confusion around MCP and what…
The explosion of AI-native applications is upon us. With each new week, massive innovations are being made in how AI-centric applications are being built. There are a variety of tools developers need to consider, be it supplying live contextual data via the Model Context Protocol (MCP) or…
MCP is a new way to integrate LLMs and AI agents with third-party data sources and APIs. It significantly improves how we build tool integrations by eliminating duplicated code and providing a centralized interface for multiple agents to access shared tools. Today, we’re excited to announce the…
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.