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While every organization races to deploy AI agents faster, a quieter crisis is compounding in the background, and it will play a large part in determining who survives the agentic era. The numbers are stark. Too many executives see AI governance as a brake on innovation or something to figure out later, after the speed problem is solved. With agentic AI, that's backwards.

While every organization races to deploy AI agents faster, finance departments are watching something alarming unfold—and it will play a large part in determining who survives the agentic era. The numbers are stark: 84% of companies report more than 6% gross margin erosion from AI costs. Within that, 26% report erosion of 16% or more. And only 15% of companies can forecast AI costs within ±10% accuracy—the majority miss by 11-25%, and nearly one in four miss by more than 50%.

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 agents without embedding fragile logic or exposing sensitive systems directly to a model. This is where a layered architecture using Volcano SDK, DataKit, and Kong MCP Proxy becomes compelling.

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 Model Context Protocol (MCP) was designed to bridge this knowledge gap by giving AI assistants access to domain-specific knowledge and capabilities they don't have built in.

See how easily Konnect Metering & Billing transforms API and AI traffic management into new revenue streams. We've talked about why 2026 is the year of AI unit economics. There, we explored the "2025 hangover" where organizations realized that without financial governance, AI isn't just a science project but has become a margin-bleeding cost center. But "governance" and "monetization" shouldn't just be buzzwords in a resolution; they need to be part of your active infrastructure.

Today, we're excited to announce Kong Mesh 2.13. Kong Mesh 2.13 delivers full support for Mesh Identity for Kubernetes and Universal mode. Plus, it's been designated as a Long Term Support release, with support for a total of 2 years. But first, what's Kong Mesh for the uninitiated?

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 now do things it couldn't before, and those same capabilities are available wherever you work. If you've used KAi before, you'll notice the difference immediately.

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 numbers tell the story. Organizations using AI in at least one business function jumped from 55% to 78% in just one year. Generative AI usage specifically rose from 33% in 2023 to 71% in 2024.

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 for AI agents is no longer sufficient.

We’re excited to announce the general availability of Kong Insomnia 12.3! Kong Insomnia 12.3 TL;DR? Your New Year’s resolution is probably something along the lines of “work smarter, not harder.” Our resolution is to make that actually possible. Insomnia 12.1 and 12.2 sharpened your tools, and now Insomnia 12.3 removes the friction to allow you to focus on building great endpoints. Less maintenance, more momentum.

The agentic era is here, and it's exposing a critical gap in enterprise infrastructure. AI agents are no longer experimental. As many as 9 out of 10 enterprise organizations are actively adopting AI agents. Agents are making autonomous decisions, orchestrating complex workflows, and interacting with dozens of services in real time.

As APIs evolve into the strategic connective tissue of digital products and AI, organizations often fail to unlock their value due to a critical misalignment between business goals and technical execution. To transform APIs into genuine growth levers, enterprises must embrace full-lifecycle API product management, prioritizing upfront strategy, robust contract design, and scalable developer experiences over mere endpoint creation.

In one of our posts, Kong AI/MCP Gateway and Kong MCP Server technical breakdown, we described the new capabilities added to Kong AI Gateway to support MCP (Model Context Protocol). The post focused exclusively on consuming MCP server and MCP tools through Kong MCP Gateway. Now, it's time to check how an AI agent can leverage the AI and MCP infrastructure exposed and protected by Kong AI/MCP Gateway.

If your 2026 resolution is to finally get AI costs under control, we've got you covered. Every January, the same resolutions show up: eat better, exercise more, finally learn that language, finally figure out that production use case for AI agents (OK, this one isn’t so typical unless you operate in our universe). But if you're responsible for your organization's AI strategy, we'd like to suggest a different one for 2026: stop letting AI be a cost center.

API endpoints process requests that power our digital world—from social feeds updating in real-time to enterprise systems syncing global inventories across continents. APIs comprise over 57% of Internet traffic, yet API endpoints often remain misunderstood. This leads to design inconsistencies, brittle integrations, and costly maintenance issues. Sound familiar? If you've merged microservices and untangled mismatched status codes, you know the pain.

While AI is revolutionizing the future of nearly every industry, it’s also created a unique set of challenges and liabilities that will need to be addressed as the area grows. Enter AI governance: a set of rules and best practices to ensure that AI is used effectively, securely, and responsibly. But what exactly does that mean, and why is it so crucial for businesses?