Working alone with Claude feels like a superpower. You open a session, give it the right context, and watch it ship. Scaling that to a whole engineering org is where it breaks. One agent with more context, more tools and more responsibility starts drifting, costs climb, and nobody can tell which part failed. So most "AI software factories" quietly ship with a team of forward-deployed engineers who wire up every customer by hand.
We built Overcut so a factory doesn't need them. Instead of one big agent, it uses small agents with clear jobs: triage, root-cause analysis, implementation, code review and verification. Each one has its own context, model and memory, so it can be measured and improved on its own. Orchestration connects them. Workflows react to events in GitHub, Jira and Slack, run in parallel, and stop at human approval gates where it matters. Because every piece is a reusable building block, teams assemble their factory from ready-made parts instead of waiting for someone to build it for them.
Then we'll prove it live. Starting from an empty project, we'll build the factory on stage: a production alert in Slack becomes a Jira ticket with a root-cause analysis, the ticket becomes a pull request, and the PR is reviewed and fixed until it's ready to merge. No FDEs, no hand-wiring.
You'll leave with architecture patterns you can apply tomorrow , whether you're one developer or rolling out agents across a whole company.