AI's ability to generate code rapidly outpaces human comprehension of its collective behavior. Applications' true functionality emerges from the interplay of various components like callbacks, queues, and state changes, a complexity that AI can now create faster than humans can track. This has fueled interest in graph engineering, where systems are modeled as graphs of deterministic components, constraining AI's freedom and improving predictability.

While making the application structure visible as a graph is a significant step, defining its execution is crucial. AI often generates locally plausible code that subtly violates global invariants due to incorrect ordering of operations. Over time, numerous AI-generated changes can lead to an emergent execution model never explicitly designed by humans.

Graph engineering, exemplified by frameworks like Lang Chain and Microsoft's Agent Framework, makes application models explicit. However, authoring the orchestration remains a challenge. Fluxtion proposes treating orchestration as a compiler problem, inferring global coordination from a closed component graph and its local event semantics. This approach, drawing inspiration from systems like Jane Street's Incremental and Dagger, aims to automatically derive the orchestrator, reducing the burden on developers.