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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.

Performance engineering teams are already stretched. AI-accelerated development means more code, more releases, and more pressure on the people responsible for making sure it all holds up. Tricentis NeoLoad 2026.2 is built around a straightforward premise: performance validation must scale to match the pace of delivery, and that means more than just making specialists faster.

AI Transport v0.5.0 is now available. It adds first-class support for running an agent turn inside a durable execution framework, such as Temporal or Vercel's Workflow Development Kit (WDK), while every client watching the conversation still sees one clean, resumable stream. The last release, v0.4.0, let an agent hydrate its history from your own database. This one is about what happens when the process running the agent isn't around for the whole turn.

Pull conversations, contact profiles, and customer events out of Crisp and into your warehouse, CRM, or AI pipeline, fully transformed, on schedule, with no engineering required. Crisp is a customer messaging platform built around a shared inbox: live chat, email, and social channels routed into one place so support, sales, and success teams can respond from a single view.

AI Transport v0.4.0 includes changes to optionally support database hydration. Some applications may wish to store AI conversation history in an external store, such as a database. AI Transport's support for database hydration allows applications to reconcile that stored history with the live activity in the AI session. When using database hydration, your application persists messages for completed runs to the database.

Having maintained a CodePush fork for 18 months and served billions of updates, we decided it was time for an overhaul. We’re big fans of CodePush, and it was a great benefit to the React Native community. But, it was written in 2015 and had some weaknesses. The biggest of these was architectural, with limitations in how update checks and release metadata could scale. The result, Codemagic Patch, is now public and available to self-host.

Every team we talk to has a running list of questions they wish they could get fast, reliable answers to. What changed in our performance last month and why. Which clients are showing the early signs of churn. Which channels are actually pulling weight and which ones are quietly burning budget. The pull toward AI for this kind of work is obvious. The answers should be a question away.

New offering enables enterprise customers to validate agentic workflows against a global network of skilled, real world testers before failures become customer, compliance, or brand crises.

Understanding complex request traces is one of the hardest parts of performance analysis. In this Release, we focused on making this significantly easier in Tideways. The Timeline has been redesigned to provide a clearer view of how requests are executed, with new layout modes, improved navigation, and a more consistent span model. These changes help you follow execution order, understand dependencies, and identify performance bottlenecks faster, even in complex applications.

We are proud to announce the release of Apache Kafka 4.3. This release contains many new features and improvements. This blog post will highlight some of the more prominent ones. For a full list of changes, be sure to check the release notes. With 25 KIPs and over 600 commits since 4.2.0, this release introduces many new features, improvements and bug fixes to all the components. See the Upgrading to 4.3 section in the documentation for the list of notable changes and detailed upgrade steps.