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Traditional observability was built around requests. A request comes in. A service processes it. A response goes out. You trace what happened in between. That model works well for APIs. It breaks down for AI. With agents, a single user request can trigger multiple model calls. An agent might invoke a tool, call another agent, query an MCP server, retry a model, hit a guardrail, and call another tool before finally producing a response. And all of this happens non-determinstically.

Today we're announcing Kong Operator 2.3. Building on 2.2's expansion into supporting Event Gateway and Dev Portal, this release significantly broadens our feature set. While 2.2 introduced foundational event-driven capabilities, 2.3 brings Kong **AI Gateway** fully into the Kubernetes-native fold, enabling teams to manage LLM routing and policies through familiar GitOps workflows. This update also rounds out Gateway API coverage with `TCPRoute`, `UDPRoute`, and `GRPCRoute`.

Every analyst has a version of this “Monday blues.” A leader pings asking for the quarterly forecast, wants it broken out by region, and needs it for a call in thirty minutes. The fastest way to do this? Pull the data out of your tool, paste it into Excel, and build the report there. Thirty minutes later, you've got the report: three new formula columns, a pivot view summarizing the regional split, and conditional formatting flagging the outliers.

At Insomnia, we believe your tools should support you (and your agents!), not force you into certain ways of working. Over the last few months, we’ve been hard at work streamlining our interface so that it’s easier than ever for you to get started, stay organized, and work however you want with your APIs. There are two key concepts when a developer works with an API.

*Kong API Gateway 3.16 is here: runtime log-level tuning, per-consumer plugin configs, credit/usage-based request blocking with the new Entitlement Enforcement plugin, and FIPS 140-3 compliance for regulated industries - all built for live production debugging and governance, no downtime or custom code required.*

Jumpstart the development of your context layer, and enable agents to reason across your entire business and provide trustworthy outputs.

Push customer profiles, orders, products, and custom events into Ometria as they happen, and pull that same data back out into your warehouse or CRM, on schedule, with no engineering required. Ometria is a customer data and marketing platform built for retail and ecommerce brands. Marketing and CRM teams use it to unify customer profiles, track order history, and trigger lifecycle campaigns based on behavior like purchases, browsing, and loyalty status.

This Release introduces AI Performance Insights, expanding the Tideways CLI with access to monitoring, exception tracking, Slow SQL, and trace data for agentic performance analysis. We added PHP 8.6 compatibility and improved trace views and instrumentation. We’ve also added a broad range of new framework- and ORM-specific bottleneck detections for Shopware, Magento, Laravel, Symfony, and Doctrine.

Imagine handing a new developer an SDK, a stack of docs, and a deadline. They read for twenty minutes, write ten lines, break something, go back to the docs, second-guess a prop name, and try again. Now multiply that by every component they'll embed, every agent, every color scheme, and every project they'll touch. That gap, between "I know what I want this to look like" and "I know the exact syntax to make it happen," is the tax every developer pays on embedded analytics.

AI has changed how fast testing teams can move, yet sorting through duplicate test cases still trips teams up. Ask the SmartBear Zephyr Agent for Rovo to generate test cases for a requirement, and coverage that used to take an afternoon comes back in a single conversation. That speed is a genuine win for teams building faster than ever, but as AI generates more test coverage, how do you make sure all of it stays worth keeping?