Systems | Development | Analytics | API | Testing

You swapped in a better model. You fine-tuned it. You threw more tokens at the problem. And still — your agents hallucinate, break under load, and deliver answers that were accurate about three hours ago. The model isn't the problem. Gartner recently flagged that up to 40% of enterprise agentic AI initiatives are at risk of failure. Executives see that number and immediately audit their LLM provider. Their infrastructure team. Their prompts.

The new Konnect Dev Portal Toolkit extension for Visual Studio Code gives you a live, portal-accurate preview of your content right beside the file you are editing. You watch a page take shape exactly as it will appear to your developers, as you type, without ever leaving your local editor.

*You wouldn't expose a REST API without a gateway. What about Kafka?* You would never expose a REST API without an API gateway in front of it. Authentication, rate limiting, observability, access control — these aren't optional extras. They're mandatory, and we've spent the last decade building the API gateway pattern to solve exactly this problem. So here's the question worth sitting with: would you expose Kafka or any event stream without a gateway?

If you've been following the agentic AI wave, you've probably noticed that the conversation tends to center on the agents themselves: which LLM to use, which orchestration framework to pick, which use cases to tackle first. But a growing body of analyst research is pointing to a different bottleneck, one that's hiding in plain sight: integration. Forrester's David Mooter argues that integration must sit at the center of your AI strategy — not as plumbing, but as a strategic capability.

As APIs grow across teams, keeping them consistent becomes difficult. Some APIs follow naming conventions and include clear descriptions, while others don’t. Over time, these differences make APIs harder to understand, review, and maintain. That is where API linting helps. That made it possible to apply custom Spectral rules as part of local development, Git workflows, or CI checks.. Teams can now upload and manage custom Spectral rulesets directly from the Insomnia UI.

Production AI no longer generates one kind of traffic. It generates four patterns, and most teams govern only one. **AI traffic management** starts with a single decision: **proxy AI traffic** through one control point instead of letting it flow straight from application code to model providers. Skip that step and security teams have no policy chokepoint, token spend climbs with no meter, and every new provider adds an integration nobody owns.

We have big news for platform and AI infra teams: *Kong AI Gateway 2.0 is available today in private beta*. It runs on its own dedicated runtime, ships on its own release cadence, and carries a completely reimagined user experience designed around the way teams actually build with AI: models, MCP servers, and agents as first-class citizens, not plugins bolted onto an API gateway.

We're thrilled to announce a strategic technology partnership between **Kong** and **ModelOp**. As enterprises rapidly transition into the agentic era, they face a critical challenge: how to deploy AI fast enough to stay competitive without taking on unacceptable regulatory or security risks. Together, **ModelOp** and **Kong** are solving the "last mile" problem of enterprise AI delivery.

Running Apache Kafka on Amazon Managed Streaming for Apache Kafka (MSK) gives you a managed broker with no ZooKeeper to operate, automated patching, and multi-AZ replication handled by the service. What it doesn't give you is a safe, governed way to expose Kafka access beyond your VPC boundary. That problem looks simple on the surface. It isn't. And how you solve it has significant implications for security posture, operational complexity, and monthly cost.

Thirteen vendors were formally evaluated. The market is projected to grow from $65 million in 2024 to **$1.434 trillion by 2030 **(in other words, this is a MAJOR emerging need)..