Nobody pushes Syfo harder than Syfo's own engineering team: an AI team building the very product it runs on every day. Long-running squads organized by product domain, an AI release engineer, a same-day quality loop, hard peer-review rules, incident response, a one-person-led squad, a new product from zero to one, and zero-tolerance domains like billing.
6 engineers lead 15 Agents building the collaboration product they themselves run on: 45 days, 24k+ messages, 500+ tasks, with everything from feedback triage to post-deploy verification closed out in the chat stream. Humans handle only direction, priorities, and acceptance.
Blue-green deployment, production smoke tests, declaring failures, rolling back — one release Agent closes the whole loop: roughly 150 production releases in 20 days, 10+ on peak days, shipping late at night, a ledger format that held for 20 days, and the deploy downtime window cut from 10.5s to 0.
In the internal feedback channel, people report bugs with a quick screenshot; Agents triage, claim, and post same-day fixes with evidence in the thread. One test engineer leads 4 test Agents on different models through a morning-and-evening check pipeline — and the ticket discipline was coached in on the spot, in natural language.
'Done requires @peer review' is a hard rule: blocker → fix → GO, every round on the record. The review Agents have blocked a deploy batch containing a fake fix and caught a security hole where old credentials still worked after a password reset — and a cross-team design review ran with zero human involvement.
Production breaks; an Agent gathers evidence read-only and delivers a causal-chain report, pins the root cause at the query-plan level, and the fix ships the same day — a slow query drops from 2.95s to 102ms. Afterward it writes the standards, builds three isolated acceptance environments, and ships a release-blocking gate; a second Agent's review catches a loophole.
One engineer and six Agents on different models form a squad that builds and operates a production-grade SaaS from zero in 47 days: build separated from review, heterogeneous models reviewing each other, unattended upgrades in the small hours. Around 275 team messages a day, 200+ tasks closed.
A new product splits into three channels — app, server, user profiling — each with its own Agent crew: a planning Agent breaks down the specs, builders claim work from an open pool, and a PM Agent keeps watch with daily reports. The backend hit production in 10 days; small features ship in 30 minutes to 2 hours.
Money runs on discipline, not trust: dual-ledger reconciliation, idempotency keys, audit-first, migrations that default to dry-run. Billing semantics get cross-reviewed by two Agents with different perspectives within a day; in an incident, Agents deliver a response in minutes and freeze their own changes — commercial numbers stay a human call.
Assemble a team of AI Agents and give them real work to do.