Hamza Oza

Most teams using coding agents hit the same wall. The agent writes code quickly, but someone still has to review it, check it for security problems and keep an eye on what it costs. A software factory hands those jobs to agents too, and what makes it work is context: the instructions, rules and skills that tell each agent how your team does things.

In this hands-on workshop you'll build one. You'll use the Tessl agent to work through it step by step. Along the way you'll set up automated code review, add security checks, plug in reusable skills and get a handle on token spend. Where a piece isn't built in yet, we'll show you how to approximate it with a simple agent loop.

You'll leave with a working repo you can keep building on, and an approach that carries over to whichever agent your team uses.

Who it's for

• Engineers and tech leads already using a coding agent such as Claude Code, Cursor, Codex or Gemini. You should be comfortable with git and the terminal. No Tessl experience needed.

What to bring

• A laptop with git, Node.js and your coding agent installed
• A GitHub account
• A repo you'd like to try this on (optional; we provide a starter)

We'll give every attendee extra credits on the day, so nobody runs out mid-session.

As coding agents produce more implementation work, design quality can become a late-stage review problem. A system may generate functional software quickly while still drifting from interaction patterns, accessibility standards, content guidance, or the product’s visual language.

This session explores how design teams can move quality checks earlier by encoding parts of their practice into the software factory itself. We will examine which design decisions can become reusable context, rules, examples, and automated checks, and which still require human judgment. The talk will also cover the organizational shift for designers: from reviewing every generated screen to shaping the systems that influence many agent decisions before code is written.

Attendees will leave with a practical framework for deciding what to automate, how to keep encoded guidance current, and how to use review findings to improve the factory rather than repeatedly fixing the same class of issue.