Vision

A semantic compilation layer for system design.

Software has mature languages for implementation, but system-level meaning still lives mostly in prose, diagrams, patterns, code, and conventions. Cohesive makes that meaning explicit as a directly inspectable semantic graph authored through readable artifacts and realized through compilers.

The Missing Semantic Layer

Teams can talk about system design in natural language. They can implement the design in programming languages. What is missing is the language layer between them: a reusable semantic representation for entities, transitions, invariants, relations, projections, policies, workflows, effects, boundaries, infrastructure requirements, and agent-facing context.

Today those constructs are usually implicit in local implementation. Because there is no shared system-level model, every layer has to rediscover and reconnect the same meaning through boilerplate, adapters, duplicated contracts, generated glue, and convention.

Cohesive represents system meaning as a connected semantic system graph of entities, relations, transitions, invariants, policies, processes, effects, boundaries, and realization requirements. The graph is authored through modular artifacts, so teams can work with focused files and views while preserving shared identities and relationships across the system.

A Language Family With Compiler Realization

Cohesive is a family of focused languages over one semantic graph. Its building blocks give each system concern the vocabulary it needs while preserving shared identity and composition across the model.

Compilers analyze the graph and produce supported implementation artifacts: code, schemas, queries, generated clients, workflows, policies, runtime bindings, tests, diagnostics, infrastructure, and agent context. Given the same model, compiler version, configuration, and declared inputs, compilation should produce the same results.

The family should evolve through a small, stable kernel and a versioned periphery. The kernel holds concepts whose composition and analysis are well understood. New or domain-specific constructs begin in the periphery with explicit lowering, assurance, compatibility, and migration rules.

Assurance And Limits

AI, explanations, diagrams, traces, and focused graph views can provide strong understanding of particular parts of a system. People and agents do not need to hold every implementation detail at once.

A durable system still needs those local views to fit together. Cohesive gives them shared identities, explicit relationships, constraints, and provenance so understanding developed in one area can connect with the rest. System-wide understanding means coherence across those views, not exhaustive detail in any one view.

Cohesive separates validation from verification. Validation asks whether the connected graph expresses what people intend. Verification asks whether generated and hand-built artifacts satisfy that graph under stated assumptions. Compilers, analyzers, tests, and runtime evidence provide different forms of assurance.

Claims and realization mappings retain their assurance: verified, derived, test-backed, declared, assumed, or opaque. Capability and portability results remain qualified as well. Cohesive should expose unsupported requirements, weakened guarantees, and unresolved semantic conflicts rather than presenting uniform certainty.

Scope And Evidence

Cohesive is intended for complex, long-lived systems where semantic drift is costly: multi-tenant SaaS, authorization-heavy products, distributed workflows, logistics, finance, and compliance. Small disposable applications may not justify the additional semantic layer.

The thesis is empirical. Cohesive should be judged by whether people and agents can understand relevant behavior and change consequences more accurately, semantic defects surface earlier, realizations remain aligned, projection drift falls, and recurring escape hatches decline over time.

Reading Path