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QUIVER introduces a formal framework for quantifying how perturbations propagate through compound AI systems structured as computation graphs, defining sensitivity matrices, trajectory divergence, bifurcation thresholds, and distribution faithfulness, with validation on production and public pipelines.
This extended paper revisits Semantic Web Services insights for Knowledge Graphs, proposing a four-dimensional formal framework and an Agentic Affordance Profile (AAP) to enable principled KG selection, composition, and failure diagnosis at agent planning time.