Systems | Development | Analytics | API | Testing

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.

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 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.

This session takes a practical look inside LLM context windows and token consumption, exploring what happens when context enters a model - from tokenization, embeddings, attention, QKV, prefill, and decode to KV caching. It also examines how context windows are allocated and why simply increasing context length doesn’t always lead to better model performance.

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.

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.