AI coding tools can build a working prototype in minutes. They also produce code that fails review, skips tests, has no clear owner, and quietly breaks when someone touches it three months later. The gap between "it demos well" and "we can ship this" is where most teams get stuck — and it's getting wider as AI output gets faster.

This session maps that gap using four concrete paradigms developers are already using — or about to. Vibe Coding is fast and creative; we show exactly where it works and where it collapses. Context Engineering improves consistency by feeding AI the right docs, schemas, and constraints. Spec-Driven Development anchors AI output to engineering standards before a line is written. Autonomous AI Agents handle multi-step tasks end to end — and introduce the hardest ownership questions of all.

The same small feature gets built four ways, live. You'll see what changes: quality, reviewability, test coverage, and time-to-own — with real code comparisons you can steal.
For engineers and tech leads at any company shipping software with AI in the loop.

By the end, you'll be able to:

- Differentiate the four AI coding paradigms by risk, speed, and governance
- Choose the right mode for exploration vs. production delivery
- Apply a decision checklist before reaching for AI on a coding task
- Evaluate AI-generated code in review with a concrete ownership lens
- Design a workflow that keeps AI output testable, reviewable, and maintainable