Writing code is no longer the bottleneck. Verifying it is. AI assistants let us generate features instantly, but that speed often creates a traffic jam of pull requests, flaky builds, and subtle bugs that look perfect on the surface but break in production. The core tension lies in balancing this new velocity with stability: how do you prevent a massive influx of machine-generated code from overwhelming your team's ability to review and secure it? This roundtable is a space for developers and tech leads to share how they are adapting their release pipelines for this high-velocity reality. We will discuss broad strategies for automating quality control, managing build infrastructure under heavy load, and designing "gatekeepers" that catch issues before a human ever has to look at them. Come share what is working (and what isn't) when building safety nets for the AI era.

TDD is not mainly about finding bugs later. The real idea is simpler: before you write any code, you write down what you expect it to do. You do this as a real example that you can run. A comment or a wiki page can quietly go out of date, and no one notices. A test cannot do that. If the code stops matching what you expected, the test fails right away and tells you loudly. That property matters even more when an AI is writing the implementation. AI coding assistants are good at generating code against a precise expectation and bad at inventing the right one from a vague prompt. A failing test is exactly that precise expectation: unambiguous, executable, and impossible for the AI to misinterpret the way it can misread a paragraph of intent. This talk walks through the real workflow I use to build production Flutter apps this way: Claude Code, driven by a skill-based orchestrator, moving through a strict 7-phase cycle (INIT, PLAN, RED, GREEN, REFACTOR, REVIEW, COMMIT) for every feature, with clean-architecture layers (repositories, use cases, Blocs/Cubits, widgets) generated to match. You'll see live, minimal RED-GREEN-REFACTOR examples across all four of those layers on a real Flutter feature, plus the quality gates (coverage thresholds, static analysis, multi-agent review) that keep the AI honest once the expectation exists. By the end, you'll leave with: - Why TDD's real purpose is stating an expectation of behavior before the code exists, not catching bugs after the fact - A concrete 7-phase TDD cycle you can adopt with any AI coding assistant - Patterns for keeping AI-generated Flutter code inside clean architecture boundaries, layer by layer - A practical way to make "write the expectation first" the only path your AI tool can take, instead of a rule it forgets