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Data analysis at scale to identify hidden fraud and achieve transaction savings.

Learn to use BigQuery’s new AI.AGG function to analyze unstructured data such as logs and documents at scale.

In the first experiment, I wanted a baseline: if an AI coding agent gets the same production signal a human would get, can it fix bugs in a codebase it has never seen? Yes, but only when I gave it better context. With only an alert, the agent passed 51% of the runtime tests. When I added captured traffic, the actual request and response for the failing call, it climbed to 77%. This post is the second pass.

AI is transforming industries at an unprecedented pace. Boards are demanding AI roadmaps, CTOs are accelerating digital transformation, and each of those initiative sits on the same foundation: data. Here’s an uncomfortable truth: many enterprises can’t tell you whether that foundation is solid. They hope and maybe even assume it is, but they can’t be certain if they aren’t testing it.

At Sapphire 2026, SAP introduced its latest vision for customers: the “Autonomous Enterprise,” a new model of business where AI agents run core ERP processes, from HR to finance and beyond. Reimagining the modern SAP enterprise also means reimagining its tools. At this year’s event, SAP renamed the Integrated Toolchain to the SAP Agent-led toolchain, revamping its role for a more intelligent enterprise.

Perforce Intelligence is in its next phase to help our customers and partners get solutions, platforms, and features that bring control at every stage of the AI software development lifecycle (AI SDLC). Read about the evolution of Perforce Intelligence and new platforms and features we’ve rolled out to make AI easier to adopt for production-ready environmental needs. Back to top.

At the recent data:unplugged 2026 in Münster, Europe’s biggest festival for data and AI, the stage was set for a masterclass in data transformation. Julian Stock, Analytics Reporting Team Lead, and Andreas Weiß, Senior Reporting Engineer, from PAYBACK, Germany’s premier loyalty program, shared the stage to detail a decade-long evolution: the journey from a strict, ticket-based reporting system to a thriving, AI-ready data culture.

A CFO asks her AI agent a simple question: "What was our ARR at the end of Q3?" The agent finds the subscriptions table, spots a column called arr, sums it up, and returns $16.4M. Strong quarter. Everyone nods. The real number was $13.9M, but no one in the room knew it yet. I hear some version of this story from nearly every data leader I talk to right now, and it almost always starts the same way. They stand up an AI pilot. It looks sharp in the POC.

This is the fourth post in our "Features Sitting Idle" series, where we explore OctoPerf features that are powerful, already available, and yet often replaced by manual workarounds. The distinction matters, and it is often overlooked in test scenarios. Teams that need to simulate a true simultaneous spike - flash sales, ticket drops, mass logins at a specific time, scheduled batch openings - usually end up working around the problem instead of using the tool's native support for it.

When we released the OctoPerf MCP Server, most teams connected to it straight from Claude.ai. Then we showed how to run the whole stack on-premise with a local model. But a question kept coming back from European teams: can we drive our load tests with a French LLM, hosted in Europe, instead of a US model? The answer is yes, and it takes about five minutes.