Systems | Development | Analytics | API | Testing

How to measure productivity in the age of agentic testing

In this AI transformation roundtable, Nate Custer, Principal Technologist at TTC Global, shares how to measure productivity on an agentic quality engineering team. Drawing on a mainframe regression story, he explains why saving time only matters if you use it to close coverage gaps. He also argues that QA teams should track how often they have to fix AI-generated code, and why that measurement might mean more than speed or headcount.

AI Workspace + Jira: Increase Jira test coverage with agentic workflows

Requirements change in Jira all the time, and QE teams have to catch every edit to update their test cases. Connect Tricentis AI Workspace agents to Jira and qTest, so any requirement change generates and updates test cases for you with the right context. In this walkthrough, Senior VP of Product Marketing Ryan Yackel, will show you how a Jira edit triggers an agentic workflow, pushes test requirements into qTest, and creates test cases with a human-in-the-loop checkpoint.

How to Turn Your Metrics Into a Monthly Executive Brief With AI Before Your Leadership Meeting

To turn your metrics into a monthly executive brief, run the Executive Decision Brief skill on your Databox data. Connect at least one revenue or core KPI source, run the skill in Genie or in Claude through Databox MCP, and schedule it monthly with a Routine. You get a one-page brief you can read in about 90 seconds: what moved, what’s working, what’s at risk, where your metrics disagree, and three decisions to put in front of the room.

Real Estate Data Enablement: Make Property Data Usable for AI, Reporting, and Automation

Which properties in your portfolio are becoming less profitable, and why? A reliable answer usually means pulling lease and occupancy data from the property management system (PMS), operating costs from accounting, maintenance records from a third platform, and rent comparables from outside the company. Some of it lives only in a spreadsheet one analyst maintains. The answer can take days, and two teams can come back with different numbers. Large real estate companies rarely lack software or data.

Control the spend and usage of your AI testing agents | SmartBear BearQ

Worried an AI teammate could rack up a surprise bill? In this video, you’ll learn how SmartBear BearQ gives you real governance over AI testing spent – built with pre-baked monthly limits, so your team always knows what to expect before a single agent gets to work.

AI FinOps: Moving Beyond Cost Visibility to Control, Optimization, and Value

AI FinOps must not be an afterthought but should be a critical and mandatory component for any enterprise AI program.. The explosive growth of AI workloads from simple AI-assisted coding to product autonomous multiagent systems complicated by highly dynamic usage-based pricing has introduced next level risk in tracking and managing spend. As the Gartner report points out, 85% of enterprises have no automated controls to stop cost spikes.