Systems | Development | Analytics | API | Testing

thoughtspot

Advancing ThoughtSpot's Commitment to Apache Ossie (Incubating), the Next Chapter of OSI

Jul 28, 2026 By Francois Lopitaux In ThoughtSpot
When the Open Semantic Interchange (OSI) initiative launched last year, it set out to solve a problem every data leader recognizes: the same business metric gets defined a dozen different ways across a company's BI tools, warehouses, and now, AI agents. "Monthly active users" in the CRM rarely matches "monthly active users" in the warehouse, and every new AI copilot added to the stack makes the gap more visible, not less. That initiative has just taken its most consequential step yet.
Read Post

Use of AI in Software Development

Jul 28, 2026 By SmartBear In SmartBear
Quick application integrity check: can your quality strategy survive the tsunami of code coming its way? AI is accelerating development, increasing code abstraction, and multiplying the volume of software teams need to validate. But existing QA approaches weren't built for this level of speed and scale. Application integrity closes the growing gap between what teams build and what they can verify, providing continuous assurance that software works as intended.
View Video
appian

Building Enterprise-Grade AI Agents: From Prototype to Production

Jul 28, 2026 By Wei Smith In Appian
Everyone can build an AI agent today. The hard part isn't getting an agent to answer a question or complete a demo. It's deploying one that employees trust, security teams approve, and operations teams can manage at scale. That's where many AI projects stall. As organizations move beyond experimentation, the conversation shifts from prompt engineering to production readiness. Can the agent safely access business data? Can you evaluate changes before deployment? Can you understand why it made a decision?
Read Post

Why Trusted Data Is the New AI Moat (w+ Rick Kranz from the AI Marketing AUtomation Lab)

Jul 28, 2026 By Databox: AI-powered analytics that answers back In Databox
Rick Kranz has built over 100 AI automations for his community and clients — he has no reason to defend Databox. But when he tried to run his AI analysis without the Databox MCP, it just stopped working. In this episode, Rick and Pete break down exactly why: the semantic layer, the metric definitions, and the standardized math that make an AI's answer trustworthy instead of a guess. If you've ever wondered why connecting five random MCP servers to Claude doesn't give you the same results as a purpose-built data layer, this is the episode.
View Video
smartbear

How agentic QA cuts the test maintenance tax

Jul 28, 2026 By Maggie Bean In SmartBear
Every QA budget has a line item for building test coverage, but 30–50% of that automation budget ends up spent on maintenance instead of new tests. That disparity stays invisible until a release goes out, the application shifts underneath the tests, and the QA team spends the next three days rewriting broken scripts instead of finding new bugs.
Read Post
qlik

Beyond the Budget: The AI Decisions That Only Humans Can Make

Jul 28, 2026 By James Fisher In Qlik
Earlier this month I spent time with a group of senior executives discussing the economics of AI: what it actually costs, where the value is and is not materialising, and what the organisations that are getting returns are doing differently from the ones that are not. That conversation encapsulates why this series is called Beyond the Budget. Not because cost does not matter. It does. But because the budget is where the consequences show up.
Read Post
antino

The Next Enterprise Cloud Is Built for Small Software

Jul 28, 2026 By Radhakanth Kodukula In Antino
The most useful piece of software we came across last quarter never left someone's laptop. An operations lead at one of our enterprise clients built herself an inventory reconciliation dashboard. No ticket, no sprint, no engineer involved. She described the workflow to an AI agent one evening and had something working by the next morning. A problem her team had been raising for over a year, solved by someone who has never written production code in her life.
Read Post
antino

How can AI agents reduce operational costs throughout the organization?

Jul 28, 2026 By Radhakanth Kodukula In Antino
‍ Every CFO has looked at the headcount report and asked the same question. Why does it take twelve people and four tools to do something that feels, on paper, like it should take three? The honest answer is usually not laziness or bloat. It is a coordination tax. Someone has to read an email, decide what it means, open three other systems to check context, type a response, update a tracker, and notify two other teams. None of that is hard work.
Read Post
ClearML

Why AI Sovereignty Is an Operational Problem

Jul 28, 2026 By Frank La Vigne In ClearML
AI sovereignty has become one of those phrases that sounds precise until someone asks what it actually means. For one federal agency, sovereignty means keeping sensitive data inside accredited boundaries. For another, it means running open-weight models in a FedRAMP-authorized private cloud. In defense and intelligence settings, it may mean operating inside an air-gapped environment at IL5 or IL6.
Read Post

Copyright © 2026 OpsMatters™. All rights reserved.