video1.0<iframe src="https://www.loom.com/embed/02b29fe331c1482abf01da402d39d22f" frameborder="0" width="1226" height="919" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>9191226Loomhttps://www.loom.com9191226https://cdn.loom.com/sessions/thumbnails/02b29fe331c1482abf01da402d39d22f-d5396c97a0b5b330.gif95.694This Loom explains a graph based agentic engine for migrating institutional quantitative code bases from C to PyTorch without months of manual engineering. It argues that naive LLM translation fails due to hallucinations, broken class hierarchies, and poor loop vectorization. The agent deterministically parses source code into a dependency graph where each function is an isolated node with mathematical primitives resolved before any generation, then replaces C loops with tensorized PyTorch broadcast operations, autograde compatible computational graphs, and synthesized unit tests. It immediately executes each migrated function against the C Oracle baseline for numerical parity and toggles cloud execution for local graphs to find parallel compilation.