AI is changing how we build software, but the value only shows up when it supports the whole lifecycle and not only code generation.

In this session we follow a single feature from a badly written user story to a merged pull request. Agents take part at every stage: refining the requirement, planning, implementing, testing and reviewing. Guardrails are present throughout the process to keep agents aligned with security, compliance, architectural and quality requirements, ensuring automation stays within agreed boundaries.

Some of that work is interactive, in the editor and the terminal. The rest runs unattended as GitHub Agentic Workflows or GitHub regular workflows.

Along the way we show how custom agents, skills and MCP integrations keep the work inside conventions the team already has, and which published workflows are worth installing before you invent your own. That is the practice. The rest is what breaks it.

In our experience the failures are rarely about the model. They are missing context, documentation that agents cannot read, decisions never written down as ADRs, repositories that all look different, prompts doing work that plain CI should own, guardrails that are either missing or applied too late, and expectations that nobody agreed. For each one we show what it looks like in a demo repository and what we do about it.

The goal is not to present AI as a shortcut, but as a practical teammate that needs the same things a new joiner needs: context, standards, guardrails, a definition of done and a review process.