Systems | Development | Analytics | API | Testing

While the benefits of LLM orchestration layers and autonomous agents are clear, they also bring a new set of non-deterministic failure modes that traditional unit testing cannot detect. A study by RAND Corporation found that 80.3% of AI projects fail to achieve the desired business outcomes, and this is because of issues in the data pipeline and model integration, not algorithmic problems.Using traditional software, you will get predictable results from known inputs.

As product delivery cadences shrink, traditional quality assurance approaches are reaching operational constraints. Traditional scripted test scripts, albeit a tried-and-true method in the past, can no longer keep up with the onslaught of dynamic code changes, changing microfrontends, and CI pipelines. In many cases, just changing a label or making a small modification to a layout may break whole integration suites and create huge backlogs.

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.

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.

Last April FloQast, an American maker of accounting software, published something unusual: a detailed engineering post on Amazon Web Services’ machine-learning blog, co-authored with AWS personnel, explaining precisely how its AI-powered transaction-matching feature works under the hood. The post described cloud infrastructure, model selection, and the specific technique (generating matching rules from user-supplied examples) that powers its AutoRec product.

Learn how to use BigQuery to analyze Gemini Enterprise usage logs, audit compliance, and build Looker dashboards at scale for your organization.

Looking back on 2025, one idea anchors everything we set out to accomplish: make data accessible and trusted so organizations can generate better decisions for their business, their customers, and the world.

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.

An effective Java debugging strategy lets us pause execution, inspect data, and observe real execution rather than relying on vague assumptions. The complexity of the Java Virtual Machine creates unique challenges, but a focused approach will turn this complexity to our advantage. This guide will equip you with the tools to do this, looking at: By the end, you’ll have a practical workflow to debug Java reliably across local and remote environments.

Amazon’s recent pivot on the Selling Partner API is an encouraging reminder that developer pushback can breach walled gardens.