2 months ago
- AI can generate code faster than humans can understand combined execution; graphs make application structure visible, and a compiler can turn that structure into a deterministic orchestrator.
- Locally plausible AI-generated code often violates global invariants, as the real behavior emerges from interactions among callbacks, timers, queues, and state changes.
- Graph engineering makes the application model explicit, but the graph alone is not the orchestrator—execution semantics, ordering, concurrency, and failure handling must still be defined.
- Fluxtion proposes inferred orchestration: the compiler analyzes a closed component graph with local event semantics and derives the global coordination, lifecycle, and reentrancy behavior as generated Java code.
- Benefits include a single source of structural truth, deterministic execution within a defined boundary, inspectable runtime artifacts, replay and audit capabilities, and a safer division of labor for AI.
- Design principle: keep probabilistic reasoning (e.g., LLMs) in explicit islands, and compile the deterministic sea around them for control and safety.
- Fluxtion combines ideas from incremental computation, compile-time dependency injection, and synchronous dataflow languages, applied to repeated event coordination across stateful Java components.
- The economic context: AI makes code generation cheap, but understanding and trusting combined behavior is expensive—graph engineering and compiled orchestration address this inversion.