A governed judgment layer for agentic products, which accounts and
capabilities to ship, throttle, or kill, priced by cost of leaving live.
Built for teams shipping assistants, workspace agents, and ops automations. Not
another success-rate dashboard, auditable GrowthDecisionRecords
with YAML-governed policy.
Override decisions with audit trail and JSONL persistence
LEARN
Experiments and Agentic Flags (CPSO, TTFV, HITL under synthetic treatments)
Outcome Flywheel (retention write-back on the same record)
Semantics Console, Taxonomy Browser, Record Inspector
Legacy analytics archive (retention, unit economics, CRO, marketplace)
Who this is for
Builders shipping agent capabilities every week
Shipping new capabilities weekly
Run success rates look fine, but nothing tells you which capability is quietly hurting
retention, or which one to kill.
Owning retention for agent seats
Trust incidents and approval fatigue pile up. You can't prioritize which problems to
fix first without a ranked list.
Watching inference burn climb
Long reasoning loops eat margin. You see aggregate token spend but not which
capabilities are uneconomic per seat.
What if
What if your weekly agent review looked like this?
Agentic products fail on verified outcomes, cost per successful outcome, and trust after
multi-step runs, not another trace explorer.
What if
Paying looked healthy, but nobody got a first win?
Revenue metrics stay green while accounts never hit a verified agent outcome. The
“AI churn wave” is often activation failure, not classic feature adoption.
What if
One intent burned a dozen metered steps?
CPSO and retry amplification show when “success” is margin-negative,
especially for power users under flat pricing.
What if
The agent “worked” until humans took over?
Rising human-in-the-loop, dismissals, and scary side effects erase trust faster than
usage dashboards admit.
The problem
You ship agent capabilities from traces and run logs, but traces don't tell you which ones to kill. Success
rates hide retention harm until seats churn.
When you throttle a capability, nothing writes back to retention. You never learn if the call helped.
The solution
churnOS emits auditable GrowthDecisionRecords:
ranked accounts and capabilities with dollar impact, YAML-governed verdicts, and an outcome flywheel that closes
the loop.
Override when you disagree. Write back retention impact; even in synthetic mode, you see how decisions
connect to churn.
Ontology as IP
Machine-readable policy, not hardcoded heuristics
Taxonomy → semantics → schema → record. Change verdict rules in YAML; the Radar
reclassifies without a code deploy.
The ontology/ package defines exception categories, vertical-specific
semantics.yaml action maps, and JSON Schema contracts for
GrowthDecisionRecord. Explore the repo via
LinkedIn.
Before → After
The shift isn't more dashboards; it's a weekly decision ritual with dollar impact and
an audit trail.
Area
Before
After
Weekly review
Run success rate dashboards
Ranked ship / throttle / kill list
Harm signal
Anecdotal support tickets
Dollar impact per capability
Policy
Hardcoded Python heuristics
YAML-governed verdicts & actions
Audit trail
Slack threads & spreadsheets
Saved decision records you can revisit
Account risk
CSM spreadsheets and gut feel
Ranked account decisions with $ impact
Retention loop
Decisions never revisit churn
Outcome flywheel writes back (demo)
Proof in production
Moovez / Quotely case study
ChurnOS-style decision-ops patterns applied to last-mile quoting after CV, OR, and SMS shipped.