Systems | Development | Analytics | API | Testing

How Astera Uses ARIA: IT Agents

IT touches almost everything in a software company, from accounts and security to infrastructure, which means even a small delay becomes a bottleneck. Sudais (Growth Strategist) sits down with Abbas Hussain (Product Solutions Engineer), to talk about how Astera uses ARIA in IT: the IT agent that Abbas helped build, how it works, and how it has become part of the IT team's day-to-day.

Ep 97 | AI Agents Move Fast. Who's in Control?

One developer burned through 300 million AI tokens in a single week. The payoff was far less impressive. For Todd Trevillion, it's a striking example of what can happen when AI adoption outpaces the controls needed to manage it. And with autonomous AI agents beginning to make decisions on behalf of businesses, the stakes are getting higher.

AI Observability in Production: How It Secures Modern Agent Workflows

Traditional software monitoring builds reliability by tracking uptime, latency, and error rates. As artificial intelligence moves into core enterprise operations, establishing robust observability in AI becomes essential to measure what systems do once live, moving far beyond basic prediction accuracy and server health. Modern non-deterministic models and autonomous AI agents act rather than just predict.

Build Embedded AI Analytics with Claude Code in 30 Min | LiveBuild | ThoughtSpot

Embedded analytics in 30 minutes: watch us embed an AI analyst and branded analytics into an app with ThoughtSpot's SpotterCode in Claude Code. Live Thursday, October 29 8am PT · 11am ET · 3pm GMT · 8:30pm IST What you'll see: Every step is real code against ThoughtSpot's Visual Embed SDK and REST APIs. No ThoughtSpot experience needed. Speaker: John Hegele, Principal Technical Architect, ThoughtSpot.

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 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 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.

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.

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.