Case study: Dramatyze

Dramatyze is a product the AISquare team built fast, in the open, while running on AISquare itself. This is a build-velocity and dogfooding story. It is not a customer governance metric. What it shows is what a small team can ship when the agent it is building leans on the same governed spine these docs describe.

The numbers come straight from the commit and work-session record below: 30.6 hours of tracked sessions (shown rounded), 569 commits (93 on the backend and 476 across the two frontend builds), and 3 contributors. They measure how the work got done, not how a customer's agents are governed in production.

What the timeline shows

The build ran in distinct phases rather than one continuous grind, which is why 30.6 tracked hours covers more than a month of calendar time.

• Backend first (Feb 11 to Feb 22). The backend started on FastAPI and moved to Django early, then settled into a steady run of commits up to the v1 release. This is the 93 backend commits.
• A real gap (a Vegas and SF trip). The record has an honest empty stretch where no commits land. The work paused and then picked back up. Velocity here is bursty, not uniform.
• Frontend v1, then a v2 rebuild (Mar 1 onward). The frontend was built, then rebuilt as v2. The v2 execution push is where the work concentrates: 229 commits in a single day, the tallest spike on the chart.
• Handoff to production (through Mar 17). v2 reached a ready handoff and then production, closing out the 476 frontend commits.

Three people did this: one on the backend, one on the first frontend, and one driving the v2 frontend rebuild. The shape of the work, long quiet stretches broken by short high-output pushes, is what you would expect from a small team shipping a real product around the rest of their lives.

Why this belongs in these docs

The point of dogfooding is that the team building AISquare ships on AISquare. The same Govern step that wraps any agent wrapped the Dramatyze agent, so its decisions landed on the reasoning and memory graph like any other run. The velocity above is the proof we care about for this page: governing an agent did not slow the team down. The agent kept running as-is while every decision started shipping traces.

What this page does not claim is a production governance metric, a blocked-attack count, or a compliance outcome for a customer. Those are real, and they live with the Trust Loop and Collective Intelligence pages where they belong. Here the honest claim is narrower and still worth making: a small team built and shipped a customer-facing product fast, on the same spine we ask you to put your agents on.

Next steps