Systems | Development | Analytics | API | Testing

Learn how to generate a client SDK for a production service when you have no documentation, no OpenAPI spec, and no remaining team knowledge of the original Ruby code. This demo shows you how to capture real production data from a running app and transform it into a functional Java client library in minutes. Visit proxymock.io OR speedscale.com to learn more.

LLMs are absorbing the business logic of microservices for agentic use cases — but both patterns will coexist in enterprise infrastructure for a long time. Cloud-native infrastructure (microservices + APIs) keeps powering web and mobile experiences. The agentic layer — LLMs, MCP tool calls, and context traffic — runs in parallel, activating the same APIs and CRUD operations underneath. Kong manages both swim lanes: the API traffic between clients and microservices, and the context traffic flowing between agents and LLMs.#Shorts.

CLI offers speed and developer freedom for API access; MCP provides centralized security, governance, and observability at enterprise scale. With CLI, credentials live on the developer's local machine and audit trails are shell-only — fast, but ungoverned. MCP adds authentication, centralized policy enforcement, and observability across all API calls, at the cost of some speed and higher token consumption. Kong's MCP Gateway is built for teams that need the governance trade-off without giving up too much velocity.#Shorts.

AI agents that never sleep are changing how businesses make decisions. Here's the full WSO2Con keynote on what that shift means for your organization. In this opening keynote from WSO2Con, the CEO walks through the transition from the digital era to what he calls the "agentic era" — where autonomous agents handle judgment calls across sales, marketing, and support. He's direct about the risks too: agents lack empathy, can make costly errors, and still need guardrails to operate safely at scale.

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.

Visit: Speedscale.com to learn more.

Stop wasting money on AI API tokens in your local, test, and CI environments. In this demo, I show how to record real LLM responses and replay them for free using proxymock and stop surprise bills.

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.