Systems | Development | Analytics | API | Testing

Designing Your Virtual Test Team

As organizations explore more advanced uses of agentic testing, a compelling vision emerges: a modular virtual test team composed of AI agents, each playing a focused role like Test Architect, Test Designer, Executor, and Summary Agent. While still early in real-world adoption, this model offers a way to coordinate intelligence at scale, with humans guiding the system and autonomy granted based on task risk and maturity.

Metrics That Matter for Agentic Testing

Traditional test metrics like automation %, pass/fail rates, and defect counts don’t reflect the impact of introducing agents into the QA process. This blog explores a new class of KPIs designed to measure how well your virtual test team is performing including Agent Assist Rate, Human Override Rate, Scenario Coverage Delta, and Review Time Saved.

Ready or Not, Here Comes CI/CD: Build Systems for an Indie Sensation

In this episode, Stephen Post, Technical Director at VOID Interactive, joins Jase for a deep dive into the technical evolution of Ready or Not, the acclaimed tactical shooter game. Stephen discusses the complexities of expanding their fully remote, globally distributed studio—from a small team of 9 to more than 75—covering key challenges such as: Whether you're a solo developer, technical lead, or project manager, this episode offers a candid and practical look at what it takes to scale a game—and a studio—without compromising on quality or agility.

Fueling the AI Future: Data, Deployment, and Tangible Outcomes with Patrick Moorhead

The future will not be decided by who experiments with AI first, but by who can operationalize it at scale - turning messy, fragmented data into trusted insights, deploying models seamlessly across hybrid environments, and delivering measurable business outcomes. To discuss, we’re joined by Patrick Moorhead, Founder, CEO and Chief Analyst at Moor Insights & Strategy.

Leveraging Confluent Cloud Schema Registry with AWS Lambda Event Source Mapping

In our previous blog post, we introduced two ways that Confluent Cloud can integrate with AWS Lambda. One option is using Lambda’s Event Source Mapping (ESM) for Apache Kafka, wherein Lambda creates a consumer group, consumes records off the provided topic, and triggers the Lambda function. The record is polled by the ESM, and the consumed record subsequently acts as the event data provided to (and processed by) the Lambda function.

Data Relationship Discovery: The Key to Better Data Modeling

Enterprise data storage comprises a patchwork of systems: ERP databases, CRM platforms, spreadsheets, cloud apps, and legacy files. These systems do their own jobs well individually, but collectively they create a fragmented landscape. For anyone tasked with building a migration, an integration, or even a simple report, the first challenge is not moving data. It’s understanding what exists and how it all connects.

AI-Powered Data Modeling: From Concept to Production Warehouse in Days

Key Takeaways Enterprise data teams spend millions on warehouse infrastructure while still designing schemas the way they did in 1995—one entity at a time, one relationship at a time, hoping the model survives its first encounter with production data. The irony runs deep: organizations racing to deploy real-time analytics are bottlenecked by modeling processes that take six to eight weeks before a single pipeline runs. Data warehouses succeed or fail on design.