Language Design
A system needs an IR
Cohesive uses the semantic system graph as an intermediate representation for entities, relations, transitions, processes, APIs, presentation, infrastructure, and AI-facing semantics.
Vision
Cohesive & AI
AI makes code abundant. It makes system understanding scarce.
As AI makes code cheaper to produce, the bottleneck shifts from writing code to understanding, constraining, coordinating, and evolving systems. Cohesive treats this as a language-design problem.
The bottleneck moves
LLM-based coding changes the economics of software. Teams can scaffold features, translate between stacks, and produce implementation code faster than before.
That does not remove the need for structure. It raises the value of structure. When code gets cheaper, the hard part becomes knowing what should exist, which constraints must hold, how changes coordinate across the system, and whether the generated artifacts still express the right meaning.
Generation
Code is a projection
The graph compiles into codebases and runtime artifacts for the technologies teams already use, rather than treating every generated file as an isolated source of meaning.
Control
Constraints must survive
Invariants, permissions, read and write sets, workflow steps, effects, and backend capabilities need to be explicit so tools and agents can preserve them under change.
Natural language is an authoring surface
Natural language is an ideal surface for intent, exploration, review, and collaboration with AI. It should not be the durable source of truth for a system.
Today, code is the operational source of truth: it is what runs, fails, scales, and gets audited. Cohesive moves the durable definition one level higher, into a semantic system graph that generates code and can be checked against it.
That distinction matters. A prompt can describe intent, but it is ambiguous and hard to diff. A codebase can execute, but its meaning is scattered across files, frameworks, conventions, and implicit assumptions. The graph gives humans, compilers, development agents, and operational agents a shared representation that is structured enough to inspect and executable enough to project.
Graph-first, not code-derived
LLMs can derive useful system graphs from existing code. Cohesive still needs that path for migrations, audits, modernization, and verification.
But code-derived graphs are downstream of implementation. They help teams navigate what exists, while also inheriting accidental complexity and depending on inference.
Cohesive works in the opposite direction. The graph comes first. Code, schemas, APIs, workflows, projections, infrastructure, and agent context are generated from it.
Graph IR
1
The system model is represented as typed, analyzable semantics rather than scattered implementation details.
Deterministic Projection
2
Compilers lower the graph into concrete code and runtime artifacts with stable names, contracts, constraints, and targets.
Code-to-Graph Loop
3
Existing code can still be analyzed back into candidate graphs for migration, comparison, and verification.
Verifiable Evolution
4
Changes can be diffed, checked, regenerated, and compared against the running system instead of only reviewed as text.
Why agents need the graph
AI coding agents benefit from proper modularity and abstraction. They are more useful when the system gives them stable boundaries, declared contracts, semantic names, explicit dependencies, and narrow places to make changes.
AI operational agents require context. They need to know which actions exist, which state transitions are legal, which effects may occur, which permissions apply, which workflows are running, and what the system is allowed to do next.
Development Agents
Work from declared semantics
A coding agent can generate, extend, refactor, and test against the graph instead of reconstructing intent from scattered code, comments, tickets, and framework conventions.
Operational Agents
Act inside system constraints
An operational agent can use the same graph as runtime context for actions, permissions, processes, effects, observations, and safe intervention paths.
The Cohesive stack for AI
The architecture is not English replacing code. It is a structured path from intent to executable systems.
Authoring
Natural language captures intent
People and AI can use natural language to explore, propose, explain, and revise system definitions.
Source Model
The graph preserves meaning
The semantic system graph records entities, relations, transitions, processes, APIs, presentation, infrastructure, and AI context in a portable model.
Execution
Code and artifacts are generated
Compilers project the graph into real codebases, schemas, APIs, workflows, infrastructure, and runtime artifacts.