A learned orchestration policy for LLM agents that jointly optimizes task decomposition and selective routing to (model, primitive) pairs, delivering 77% macro pass@1 at 10x lower cost than strong baselines across 13 benchmarks.
The orchestration of multi-agent systems: Architectures, protocols, and enterprise adoption
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Modality-native routing in A2A networks raises task accuracy from 32% to 52% over text-bottleneck baselines on a 50-task benchmark, but only when paired with capable downstream reasoning.
Context Kubernetes formalizes six abstractions for knowledge orchestration in agentic AI, with experiments showing a three-tier permission model blocks all five tested attack scenarios where simpler baselines fail.
Agentic AI evaluation and governance lack mechanisms to bind obligations to actions and prove compliance at runtime; a new synthesis framework with ODTA criteria and action-evidence bundles addresses this closure gap.
citing papers explorer
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Uno-Orchestra: Parsimonious Agent Routing via Selective Delegation
A learned orchestration policy for LLM agents that jointly optimizes task decomposition and selective routing to (model, primitive) pairs, delivering 77% macro pass@1 at 10x lower cost than strong baselines across 13 benchmarks.
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Modality-Native Routing in Agent-to-Agent Networks: A Multimodal A2A Protocol Extension
Modality-native routing in A2A networks raises task accuracy from 32% to 52% over text-bottleneck baselines on a 50-task benchmark, but only when paired with capable downstream reasoning.
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Context Kubernetes: Declarative Orchestration of Enterprise Knowledge for Agentic AI Systems
Context Kubernetes formalizes six abstractions for knowledge orchestration in agentic AI, with experiments showing a three-tier permission model blocks all five tested attack scenarios where simpler baselines fail.
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Beyond Task Success: An Evidence-Synthesis Framework for Evaluating, Governing, and Orchestrating Agentic AI
Agentic AI evaluation and governance lack mechanisms to bind obligations to actions and prove compliance at runtime; a new synthesis framework with ODTA criteria and action-evidence bundles addresses this closure gap.