Systems | Development | Analytics | API | Testing

RAG in Quality Engineering: Ship Faster, Test Smarter | Janani Balasubramanian

How can Retrieval-Augmented Generation (RAG) help Quality Engineering teams ship faster and test smarter? In this TTTribeCast session, Janani Balasubramanian explores the practical applications of RAG in Quality Engineering and how teams can use organizational knowledge, testing data, and engineering context to improve the way they design, prioritize, and execute testing.

Guide to Load Testing Multi-Tenant SaaS Applications (2026)

Multi-tenant SaaS applications require a fundamentally different approach to load testing. Standard scripts that ignore tenant boundaries often miss critical issues. Tenant-aware load testing is essential for accurately measuring both overall performance and the integrity of data isolation. Without this, subtle forms of cross-tenant data leakage or resource contention may go undetected, especially under realistic, mixed-tenant workloads.

Team-Based DLP: Give Each Group Its Own Redaction Rules

A shared Kubernetes cluster rarely belongs to one team. Payments runs checkout in one namespace, search runs search-api in another, and a risk team runs a scorer somewhere else. One Speedscale forwarder captures API traffic for all of them. Redacting that traffic before it leaves the cluster is what makes it safe to use for testing (the background is in The PII Testing Dilemma). Until now, that forwarder ran exactly one DLP rule. Every team that needed a field redacted had to edit the same JSON document.

How to Build a Marketing Dashboard in One Conversation

Describe the metrics in plain English. AI Analyst builds them, assembles the dashboard, and next month’s report starts from this month’s build. To build a marketing dashboard in one conversation, describe each metric you need to AI Analyst inside Databox in plain English, confirm the components it proposes, and ask for a dashboard assembled from the metrics you defined.

From Experiment Tracking to AI Factory: What Changes When AI Becomes a Shared Enterprise Capability

The term “AI factory” is usually introduced as a hardware story: racks of accelerators, high-speed networking, and validated reference designs. That part is real, but it is not the part most organizations struggle with. The harder shift is operational. Moving from AI as a set of individual experiments to AI as a shared, governed capability that many teams depend on changes how compute is allocated, how environments are built, who owns the platform, and how the work is governed.

BOAT Defines a New Future for Automation: Appian is a Leader in the 2026 Gartner Magic Quadrant Report

Over the past few decades of digital transformation, organizations have accumulated separate tools for separate jobs: RPA for UI automation, BPM for workflow management, iPaaS for API integrations, and other point solutions. These investments have delivered undeniable productivity gains. But they've also created challenges: systems that don’t talk to each other, processes that break across integrations, and IT teams that spend more time maintaining the technology rather than innovating with it.

End-to-End Test Orchestration using MCP Servers | Raghunath Chilkuru

Most QA teams are still switching between requirement docs, their local codebase, and CI/CD dashboards to get automation done. This session is about closing that gap -using AI not as a code generator you prompt occasionally, but as something closer to an actual QA teammate working inside your IDE. ​Key Takeways:​A working understanding of MCP architecture - how to configure and run local or cloud-based MCP servers to connect your IDE with enterprise tools.

AI Adoption: What Goes Wrong & How Leaders Fix It | Brenn Hill

In this interactive AMA session, Brenn Hill, AI executive and author of The Delivery Gap, explores why many AI adoption initiatives fail to create lasting impact despite growing investment and enthusiasm. Drawing from his experience helping engineering organizations adopt AI at scale, Brenn unpacks the common pitfalls that hold teams back and shares practical strategies for engineering leaders to drive meaningful adoption. The session will cover how to align AI with business goals, measure success beyond hype, and build a culture that enables sustainable AI-driven transformation.