Rethinking Analytics For The Agentic Era
Databox is now an agentic analytics platform. AI that analyzes, reports, and acts on your performance, built on your data, your metrics, and your business context.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Why Your AI Strategy Needs Both Shared and Shared-Nothing Storage
As enterprises scale AI initiatives from the core data center to the edge, many organizations are thwarted by weak data practices and fragmented systems. The attempt to use a one-size-fits-all architectural approach for workloads that have fundamentally different needs is no longer a solution. If your AI ambitions are outgrowing your storage: the solution isn’t just adding more capacity, but matching the right architecture to the right stage of the AI lifecycle.
Ep 89 | Running with Scissors: The Enterprise AI Reality
Enterprise AI loves a shiny object. Sol Rashidi would rather talk about procurement. After years of leading AI deployments, Sol has learned that some of the biggest wins come from the decidedly unglamorous parts of the business. Her favorite function to transform? Procurement.
Testing the Tester: Who Validates the Humans Evaluating AI?
What calibration runs have taught us about human judgment, ground truth, and the hidden QA problem inside AI evaluation.
Closing the gap to AI speed with a holistic test management approach
Every AI headline this year is about speed: code shipping faster, test scripts generating in seconds, releases compressing from weeks to days. What gets less attention is what happens to quality when everything around it accelerates and the way you manage testing doesn’t.
Testing at AI speed: We built a drift detection capability, then used it on ourselves
Drift detection stays narrow on purpose. It checks contract conformance – structure, status codes, schema – and deliberately leaves workflows and business logic to functional and end-to-end tests. That’s what makes it a fast add to our pipeline to prevent API drift. That narrow scope keeps it cheap to run. Fast execution, low flakiness, low maintenance cost – the kind of checks that make you more confident shipping. Catching drift early is the whole point.
Top 17 AI Testing Tools
Generating tests isn’t the hard part anymore. Ask an LLM to create Playwright tests, and it will produce hundreds of them in no time. The problem lies in deciding whether those tests are useful, maintaining them as your application changes, and knowing if you’ve even chosen the right tool in the first place. This article compares 17 AI testing tools across 5 categories. We’ll look at what each of them does, where it fits, what makes it different, and where it falls short.
What's Next Is Not More AI. It's Better Foundations.
The next real advantage in artificial intelligence will not come from the next AI tool or application. It will come from a stronger data foundation beneath it. At Hitachi Vantara we work every day with customers on the data supporting their systems. That vantage point has led me to a simple conclusion: the leaders who pull ahead will not be the ones with the most advanced AI. The leaders will be those whose data foundations are strong enough so that AI can be trusted to act.
Cheap Tokens, Exploding Bills: The Hidden Cost of AI Agents
80% cheaper AI model. Nearly 2X the data warehouse bill. Switching to a cheaper AI model can cut your token costs by 80%, but it might accidentally double your public cloud data warehouse bill. In this video, we break down the "Agentic Cost Shift"—how autonomous AI agents generate hidden, high-compute SQL queries that drive up data lakehouse costs—and how to fix it using Cloudera. See where the real cost of AI is shifting.
Stop your AI agent's $100,000 reordering mistake
See how Fivetran and dbt help an AI reordering agent make more reliable retail decisions. This end-to-end demo shows how trusted data supports smarter inventory decisions across the enterprise.