Teaching the physical world to think.
These working notes describe the control objectives and research patterns behind Fibric. They are reference architecture, not evidence that every control is deployed across every connector or external system. BearScope is the current live product; broader platform paths remain managed early access.
Three problems we had to solve.
Neuro-symbolic root-cause analysis
A language model is good at hypotheses and bad at certainty. A symbolic system is the reverse. We combine them so Fibric can explain why an operation is going wrong, with a chain you can audit rather than a guess you have to trust.
Bounded duplicate suppression for agent effects
Exactly-once claims break at external boundaries. This note describes how idempotency, single-flight, downstream cooperation, and explicit ambiguity handling can reduce duplicate effects without pretending to guarantee them end to end.
Fail-closed governed autonomy
Governed autonomy needs a verified separation between model proposals, policy checks, and execution. This note describes that control pattern, its deployment assumptions, and why high-consequence paths still need review and independent safeguards.
Borrow what's proven. Build what's missing.
Grounded in the literature
Each note traces to real ideas from distributed systems and AI research. Where a proven wheel exists we use it, and point it at the physical world.
The screen is not the edge
Most agent research assumes the world ends at an API. Ours starts where it does not, where an action opens a door, ships an order, or sends a real message that cannot be unsent.
Trust is a property, not a promise
We want controls a deployment can test: deterministic checks where appropriate, records on supported action paths, and boundaries whose coverage is explicit.