Systems | Development | Analytics | API | Testing

Anthropic Acquires Stainless. What's It Mean for AI Connectivity?

Every few months, a frontier AI lab makes a move that says the quiet part out loud: agents are only as useful as the systems they can reach. The latest example is Anthropic's acquisition of Stainless, the company behind the tooling that turns API specs into SDKs and MCP servers. Anthropic's own framing is direct. Agents need to connect to data and tools, and the path from an API to an agent-ready interface needs to get shorter. We agree. We've been making a version of this argument for two years.

How Headless Software Powers the Machine Internet

Software is going headless: the internet is shifting from GUIs built for humans to APIs, MCP servers, and CLIs built for machines and agents. Machines will consume the internet at a scale 1,000x greater than humans — more agents will exist than people, and programmatic access moves far more data than any click ever could. This transition requires API and AI infrastructure capable of moving terabytes at a scale never built before. Kong provides the connectivity layer for this machine internet — the infrastructure between agents, LLMs, and the services they consume.#Shorts.

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

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.

A Unified Gateway for APIs and Agentic Applications on VMware VKS with Kong Konnect

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.

How to Talk to Your CFO About AI Gateway Metrics Without Losing Them in the First Slide

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.

Your AI Agent Knows What. It Doesn't Know Why.

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.

What is an MCP Registry? The Centralized Directory for AI Agents

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?

How to set up Billing for AI Agents with LangChain and Kong in 15 Minutes | Monetize AI Agents

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.

Stop Subsidizing Innovation, Start Monetizing It

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.