Domain model
The product defines the recurring shape of the customer’s job. Each customer builds a unique working instance through use.
Agentic data primitives
Goals, evidence, decisions, artifacts, dependencies, and outcomes become product state that agents and customers can keep using.
Product-owned work
Retrieved context helps an agent answer once. These primitives give the product current state, explicit relationships, authority, and rules for what changes next.
The product defines the recurring shape of the customer’s job. Each customer builds a unique working instance through use.
Goals, evidence, decisions, artifacts, open work, actions, and outcomes become explicit product-owned state.
Relationships state what informed each artifact and which downstream work should move when an input changes.
Current state, historical versions, provenance, and authority make human judgment inspectable and durable.
Each execution receives a narrow context package containing the work it can read and change.
Stable execution identity and selective regeneration preserve valid work while affected work updates.
Review, correction, approval, and redirection happen on the same work the AI assistant is performing.
Deeper implementation patterns
Order, decomposition, synthesis, reuse, governance, distillation, locking, and alternatives become repeatable ways to maintain customer work.
Linear progression of thought with visible continuation.
Resolve independent components before combining them.
Form a unified outcome from multiple inputs.
Apply an established reasoning structure to a new problem.
Apply shared structural checks after primary reasoning.
Extract a concise insight and reuse it in subsequent thinking.
Preserve upstream results so downstream reasoning can proceed without recomputation.
Evaluate structured variations before choosing direction.