Systems | Development | Analytics | API | Testing

While AI coding tools dramatically slash development time, they are quietly inflating testing and maintenance burdens because teams no longer fully understand their codebase. Discover how leading engineering teams are shifting focus to testing and context packages to eliminate bottlenecks and unlock true AI efficiency.

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.

Watch an AI agent in Astera Centerprise map data from Dynamics CRM to Snowflake, field by field, with data transformation, in a no-code interface. This demo walks through a real data mapping scenario: a sales team's pipeline in Dynamics CRM (accounts, deal stage, close date, and deal amount in multiple currencies) needs to land in Snowflake every night for revenue and forecast dashboards. See how the agent reads both schemas, matches fields automatically, standardizes currency, and schedules the pipeline to run unattended.

AI agents can write tests, but those tests can repeat the same assumptions that led to the code. In this walkthrough, I use Cursor and proxymock to replay real application traffic against a local Go app, compare requests and responses, and validate code changes with real data.

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.

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.

When a performance test run goes wrong, the real work begins, which means hours of manual analysis, digging through metrics, and trying to prioritize what to fix first. Agentic Performance Testing (APT) in NeoLoad changes that. In this demo, see how APT's specialized AI agents automatically analyze a failed test complete with a full, stakeholder-ready report, surfacing an executive summary, SLA compliance breakdown, trend analysis. critical findings, a prioritized action plan, and more, all without leaving NeoLoad.

AI writes code faster than any team can check it. Diego Molina thought he had a tooling problem. He didn't. In 37 minutes he shows the workflow mistake most engineering teams are making right now, the one bottleneck that decides whether AI speeds you up or buries you, and the principles that drive his own workflow scale without the noise. Chapters Learn more at saucelabs.com.

Rovo, Atlassian’s AI assistant, can help you generate test cases directly inside Jira through the SmartBear Zephyr Agent for Rovo. This demo offers a practical look at AI-powered testing with Rovo in Jira, from requirements to reviewed test cases, all within the Jira experience, without switching tools.

Most AI pilots never make it past the demo phase. AWS Machine Learning Lead Praveen Jayakumar has seen plenty of promising AI projects get stuck between a successful demo and production. Teams often define what success looks like without deciding what failure looks like, leaving underperforming projects alive long after they should have been shut down. As Praveen puts it, they become “zombie” AI projects.