Most developers use AI like a smarter autocomplete. I've been using AI agents differently: as members of a small product team, each with a defined role (planning, implementation, review, testing, documentation) and clear hand-offs and quality gates between them. Working this way, I've shipped production mobile apps and backend platforms with a fraction of a traditional team.

This talk is a practitioner's walkthrough of that workflow, not a vendor demo. It covers how to break work down so agents can own it, the review and test gates that keep quality high, and where agents reliably fail: architecture decisions, ambiguous requirements and security. That is why a human stays accountable for every merge. I'll share real examples, including what went wrong, and give a framework for deciding which roles to hand to AI and which to keep.

Key takeaways:
1. A role-based structure for AI-assisted development with clear hand-offs
2. Quality gates (tests, reviews, specs) that make agent output safe to ship
3. A checklist for which work to delegate and which a human must own