Safety-gated autonomous operations
Safety is architecture, not policy: every dangerous motion is gated by a structural barrier — a required approval token, a fail-closed derivation, a single audited write path — so safety holds even if someone later writes careless code.
I built a safety-gated autonomous operations platform over 16+ sprints: a multi-module Python system (PostgreSQL, mypy --strict, full pytest coverage) with safety enforced architecturally — every high-impact action gated behind cryptographically-verified human approval, fail-closed scope, and a single audited path that structurally prevents fabricated results.
Propose, don't dispose
On the safety-gated platform, no autonomous component can execute a consequential action; it can only propose. A two-step, cryptographically gated human approval is required before anything with external effect happens.
Fail-closed scope
Scope enforcement is fail-closed: the system derives what's in-scope from an authoritative source and refuses to act outside it. When scope is uncertain, it stops rather than guesses.
Evidence, not fabrication
The analytical layer is structurally prevented from inventing findings: there is a single, audited path for producing results, and every result must trace to real evidence. A component that would assert something unsupported is blocked.
Where the data genuinely can't answer a question, the system records "unknown" and carries that uncertainty forward — it never coerces an unknown into a false-confident number.
Bounded autonomy
The autonomous layer runs under enforced cost brakes and an enable-gate, with a static-analysis test proving it cannot reach outside its permitted surface.
How it was delivered
That platform was delivered in 16 structured sprints, each with checkable acceptance criteria and quality gates.
That codebase is private; an architecture walkthrough is available under NDA.