Systems | Development | Analytics | API | Testing

What Is a Context Graph and Why Does AI Need One?

May 21, 2026 By Kong In Kong
The context graph — not the UI layer or system of record — is the true competitive IP of the AI era, and Kong built Context Mesh to help companies govern it. Without the right context layer, AI agents are generic and interchangeable regardless of which LLM is underneath. Companies that own and protect their context graph can differentiate their agentic workflows; those that don't are left with legacy CRUD backends that don't translate to agentic use cases. Context Mesh gives enterprises policy and governance over what agents can consume — the rulebook for all context flowing in and out.#Shorts.
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A Unified Gateway for APIs and Agentic Applications on VMware VKS with Kong Konnect

May 20, 2026 By Claudio Acquaviva In Kong
Customers today face significant challenges as their Kubernetes environments scale. The proliferation of microservices, external integrations, and new AI workloads increases traffic volume and connectivity complexity, creating material risks to performance and availability. The core issue is a lack of end-to-end governance: as diverse workloads expand, unmanaged interactions make it difficult to apply consistent security and enforce global consumption policies.
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How to Talk to Your CFO About AI Gateway Metrics Without Losing Them in the First Slide

May 19, 2026 By Dan Temkin In Kong
Your AI infrastructure is producing financial signals your CFO has never seen. Token consumption is a direct cost line item. Cache hit rate is a margin improvement. Model routing decisions are cost arbitrage events. These things are happening right now, in the gateway layer, with no route to the CFO, which means no route to the boardroom. As the AI connectivity platform owner, you're the person who can build that route.
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Your AI Agent Knows What. It Doesn't Know Why.

May 19, 2026 By Hugo Guerrero In Kong
There's a reason we don't find our keys by scanning every room like a security camera. We replay the tape. We remember the groceries, the front door, the distraction. We reconstruct the *why* to find the *where*. Our brains are commit logs, not snapshots. Most agentic AI systems today work more like the camera — a static frame of the world at a given moment. They store state. They retrieve context. They produce an answer.
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What is an MCP Registry? The Centralized Directory for AI Agents

May 13, 2026 By Kong In Kong
A guide to learning how MCP registries help govern AI agent-to-tool connectivity AI agents are only as capable as the tools they can reach. When an agent needs to query a database, file a support ticket, or pull data from a CRM, it has to find the right tool, authenticate, and invoke it — all at runtime. The Model Context Protocol (MCP) standardizes how agents communicate with these tools. But MCP alone does not answer a fundamental question: how does the agent know which tools exist?
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How to set up Billing for AI Agents with LangChain and Kong in 15 Minutes | Monetize AI Agents

May 12, 2026 By Kong In Kong
Want to bill customers for the AI tokens they actually use? This video shows you how to set up a LangChain app that meters LLM token usage and streams it to Kong Konnect Metering & Billing as CloudEvents — turning every prompt and response into invoiced usage, automatically.
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Stop Subsidizing Innovation, Start Monetizing It

May 11, 2026 By Dan Temkin In Kong
The ‘AI Credit’ Economy: GitHub’s Pricing Shift Is the Beginning, Not the Exception *GitHub just sent waves of budget panic across its developer base. Seat-based Copilot pricing is out. Consumption-based credits are in. And if you're building an AI-driven product today on flat-rate pricing? You're building a problem into your roadmap.* Seats aren't going away, but they now fund a shared pool of AI credits (one credit = one cent) instead of unlocking uncapped use.
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AI Agent Integration: Gartner Research Confirms Need for AI Control Layer

May 8, 2026 By Heather Halenbeck In Kong
Three-quarters of enterprises are now piloting or deploying AI agents. But here’s the problem: actually integrating those agents with enterprise applications is proving to be one of the hardest parts of the whole endeavor. The research doesn’t mince words about the challenge. And it maps directly to the infrastructure gap Kong was built to address..
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Building a Secure, Scalable AI Infrastructure with Kong and Akamai: A Technical Introduction

May 4, 2026 By Claudio Acquaviva In Kong
As organizations transition from experimental AI to production-grade systems, they often face a fragmented landscape of unmanaged LLM providers, complex tool integrations, and escalating security risks. This infrastructure gap leaves AI applications vulnerable to sophisticated threats like prompt injection and data exfiltration, necessitating a unified stack that secures the edge while streamlining the data plane..
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