Requirements for a Shared System Model

A shared system model must remain useful across consumers with different capabilities and release cycles. That requires stronger boundaries than a single tool's internal cache.

R1 — Stable identity

Projects, services, contracts, policies, artifacts, and evidence need stable identities. Paths may locate an entity, but path alone is insufficient when a repository moves, a workspace spans roots, or multiple artifacts represent the same logical boundary.

R2 — Versioned contracts

Every durable exchange shape needs a schema identifier and compatibility rule. Consumers must be able to reject unsupported versions rather than silently misread fields.

R3 — Evidence provenance

Facts must retain source, scope, observation revision, confidence, and freshness. Inference should be first-class and explicitly different from verification.

R4 — Typed relationships

An untyped edge is difficult to govern. depends-on, produces, consumes, owns, governs, affects, and verifies have different invalidation and decision semantics.

R5 — Deterministic validation

AI may enrich the model, but schema checks, referential integrity, identity rules, compatibility, and evidence freshness need deterministic validation.

R6 — Scoped projections

Consumers should request views appropriate to their purpose without duplicating the canonical model. A projection must identify its scope, model revision, and omitted or unknown information.

R7 — Controlled mutation

Every producer needs declared capabilities. Observers can propose facts; validators can promote or reject them; renderers can transform views without mutating canonical truth.

R8 — Incremental invalidation

Large workspaces cannot rebuild all intelligence after every file change. The model needs dependency-aware invalidation so affected observations and interpretations become stale while unrelated evidence remains reusable.

R9 — Explainability

A consumer should be able to trace an important answer to model entities, relationships, and evidence. Explainability is a data lineage requirement, not only a natural-language explanation.

R10 — Tool independence

The logical model must not depend on one IDE file, agent prompt, workflow engine, or vendor SDK. Adapters render the shared contract into consumer-native forms.

Together these requirements create an interoperability boundary: one model can support many tools without allowing any one consumer to redefine system truth.

Method

The requirements were produced through boundary analysis across four consumer classes: developer interfaces, CI and release automation, IDE integrations, and AI agents. Each consumer was evaluated for what it reads, what it may write, how quickly it changes, and which failure would be unsafe. Requirements were retained when at least two consumer classes needed the same invariant or when violating the invariant could corrupt shared system meaning.

The analysis also separates logical requirements from rendering choices. For example, an agent instruction file and a CI JSON report may use different formats, but both must preserve model revision, entity identity, evidence state, and contract version. This distinction prevents a consumer-specific file layout from becoming the canonical model.

The ten requirements can be evaluated with a conformance matrix. Rows contain model entities, relationships, evidence, projections, and mutations. Columns contain identity stability, schema versioning, provenance, validation, invalidation, and explainability. Every supported operation must identify the cells it reads or changes. An empty cell is reported as unsupported or unknown, not inferred from implementation convenience.

Limitations

These requirements define architectural properties, not a complete protocol. They do not specify distributed consensus, cross-organization authorization, retention policy, or the economics of storing historical evidence. A local workspace can satisfy the model while still requiring additional controls when evidence crosses trust domains.

Stable identity also introduces migration obligations. Implementations must define how identities survive repository moves, splits, merges, and imported history; the requirement alone does not choose that algorithm. Incremental invalidation depends on sufficiently complete relationships, so early models may need conservative invalidation to avoid reusing affected evidence. Tool independence should therefore be treated as a compatibility discipline, not a claim that every consumer can use every capability.

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