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. Without a centralized orchestration layer, developers struggle to maintain consistent…
[](/blog/engineering/ai-infrastructure-with-kong-and-akamai)
Scaling LLM and agentic AI adoption from pilot programs to enterprise-wide deployments is a massive logistical rollout. As AI and agentic usage grow, so does a nagging question for leadership: Are agents using the right tools to get the job done? While raw infrastructure metrics might tell you if a…
[](/blog/product-releases/kong-ai-governance-metrics-a2a-mcp)
The shift from single-model AI features to multi-agent pipelines is no longer a future concern — it's running in production today. MCP has become the de facto protocol for tool-calling, agent-to-agent (A2A) communication patterns are proliferating, and enterprise teams are wiring together complex…
[](/blog/product-releases/kong-ai-gateway-3-14)
When enterprises started experimenting with LLMs, the governance challenge — while still difficult — was relatively contained once you had an AI Gateway for LLM traffic. You had a request going to a model, a response coming back, and a gateway in between to enforce policy. With the right solutions,…
[](/blog/product-releases/kong-agent-gateway)
You whipped up a simple MCP server prototype over the weekend. It routed a single AI agent to a few internal tools, your demo impressed leadership, and the team asked the dreaded question: "When can we ship?" You smiled and said, "Give me two weeks." Fast-forward three months . You’re firefighting…
[](/blog/enterprise/build-vs-buy-mcp-server-infrastructure)
Every few weeks, the AI-adjacent internet crowns a new corpse. SaaS is dead. OpenAI is dead. The IDE is dead. Humans writing code is dead. The structure is always the same: take a real observation, strip it of all nuance, and toll the death knell to feed the hype train. Rinse and repeat. The…
[](/blog/enterprise/cli-vs-mcp-enterprise-ai-governance)
As enterprises start deploying AI agents into real systems, a new architectural challenge is emerging. Agents need a reliable way to discover tools, services, and capabilities dynamically, instead of relying on hardcoded integrations. This is where the Model Context Protocol (MCP) ecosystem is…
[](/blog/engineering/mcp-registry-dynamic-tool-discovery)
Last week, Gartner published a report titled MCP Servers Will Fuel the Next AI Revenue Surge — Context as a Service (1) that should be required reading for every enterprise technology leader. Then, Kong CEO Augusto Marietti (Aghi for short) wrote out his thoughts on the subject and why context is…
[](/blog/enterprise/the-platform-enterprises-need-to-compete)
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…
[](/blog/learning-center/what-is-a-mcp-gateway)
The promise of agentic AI is clear: autonomous systems that can reason, plan, and act on your behalf. But there's a fundamental problem standing between that vision and enterprise reality: agents need context to make decisions, and that context lives scattered across your organization. Context is…
[](/blog/product-releases/introducing-kong-context-mesh)
For too long, the Model Context Protocol (MCP) has operated on a principle of open access: connect an AI agent to an MCP server, and it gets access to every single tool that server offers. While this approach is simple for initial experimentation, it quickly becomes a liability in production.…
[](/blog/engineering/mcp-tool-governance-security-meets-context-efficiency)
In the rapidly evolving landscape of AI-driven development, the Model Context Protocol (MCP) has emerged as the critical standard for connecting AI applications to the data and tools they need. We are excited to announce the Technical Preview (TP) of Kong MCP Registry , a major milestone in our…
[](/blog/product-releases/kong-mcp-registry-tech-preview)
Modern AI applications are no longer just about sending prompts to an LLM and returning text. As soon as AI systems need to interact with real business data, internal APIs, or operational workflows, the problem becomes one of orchestration, security, and control. The challenge is to build secure AI…
[](/blog/engineering/secure-ai-agents-volcano-sdk-kong-mcp-proxy)
Have you ever asked an AI assistant to generate code for a framework it doesn't quite understand? Maybe it produces something that looks right, but the syntax is slightly off, or it uses deprecated patterns. The AI is working hard, but it lacks the specific context it needs to truly help you. The…
[](/blog/engineering/mcp-servers-guide)
KAi, the AI assistant inside Kong Konnect, just got significantly more capable. Today, we're announcing an enhanced beta version powered by the new Kong Konnect MCP Server — a shared infrastructure layer that also opens up your API platform to IDE copilots and custom agents . The result? KAi can…
[](/blog/product-releases/kai-upgrade-powered-by-konnect-mcp-server)
Gartner just published research that should be required reading for every platform and infrastructure leader building for the agentic era. The report, " How to Enable Agentic AI via API-Based Integration ," makes a stark claim: incrementally reworking existing APIs and connector-based integrations…
[](/blog/enterprise/gartners-context-mesh)