Aloo Paratha

Multi-agent handoffs today rely on flat prose summaries. This lossy projection discards structural graph dependencies and resource constraints, forcing receiving agents to guess step order and rules. This causes a 51.3% failure rate under task-specification constraints. Appending growing conversational histories also causes severe KV-cache pollution and context bloat. The Solution (Mao) Mao is a plug-and-play middleware SDK that replaces prose handoffs with a joint structural-semantic vector space (128-dimensional). It projects a relation-aware graph attention network (R-GAT) representation of the active task frontier and aligns it contrastively with agent reasoning traces using a triplet margin loss. This clusters valid actions close to the graph state while pushing violating actions far away. Local Safety & Witness-Routing At runtime, Mao measures the cosine similarity between the graph and the proposed action. For safety, it uses a hybrid neuro-symbolic gate called witness-routing. An action is only rejected if the neural gate fires and the symbolic graph checker can name the specific violated constraint edge. This ensures a flawless gating precision of 1.000. On rejection, local Gemma 4 (via Ollama) translates raw topological violations into natural-language replan instructions. Key Metrics (n=300 held-out samples) Baseline Prose Handoff Success: 48.7% Mao Local Offline Success: 99.7% with a median latency of 1.7 ms. Mao Gemini Cloud Success: 99.3% with a median latency of 527 ms. Zero-Shot Domain Generalization (LOTO): Averages 95.1% success on unseen workflows. Out-of-Distribution Safety: Deferral rate safely climbs from 6% to 62% under domain shift rather than failing silently, keeping precision at 1.000.000 precision everywhere.