Based on the Causal Reachability Model
Deterministic Decision Kernel for Replayable Human Judgment
Infrastructure for replayable, immutable, auditable decision graphs — defining what is reachable, not just what is allowed.
Core Concepts
Foundational ideas and operational structures for AI-native systems.
From defining what is possible → to controlling it → to redesigning the web.
Theory
Define what is reachable. Security emerges from structure, not rules.
Explore the model
→ Concept
Graph Traversal Control→ Algorithm Spec
GTCA v0.2→ Runtime Layer
Incremental Reachability EngineOperations
Operationalize reachability. Control what AI can see and do in real time.
Vision
Extend reachability to the internet. From URLs to meaning-based routing.
Same input → same output. Stable ordering and replay across time.
After commit, the graph is append-only. Changes are expressed via supersession.
Reconstruct graph state as-of a boundary (commitId / timestamp) deterministically.
DecisionGraph Core validates the structure of decisions — not just code. If a decision depends on a superseded assumption, CI fails deterministically. The code didn't change. The assumption did.
The code did not change. The assumption did.
Ecosystem
Companion projects built on top of DecisionGraph Core.
Built on DecisionGraph Core — an append-only WHY engine for causal evidence, replay, and audit trails.
v0.4.8 Upgraded `actions/checkout` and `actions/setup-node` to v5 across all workflows