JADE: Bridging the Strategic-Operational Gap in Dynamic Agentic RAG

2026-01-29

AI Summary

This work addresses the performance limitations of existing dynamic agent-based RAG systems, which stem from the mismatch between planning strategies and execution capabilities due to their independent optimization. To overcome this, we propose JADE, a novel framework that models dynamic multi-turn RAG as a collaborative multi-agent team sharing a unified backbone. By leveraging end-to-end reinforcement learning with outcome-based rewards, JADE enables joint optimization of planning and execution. The approach introduces dynamic workflow orchestration, effectively bridging the gap between strategic intent and operational capacity. This integration not only maintains computational efficiency but also significantly enhances overall system performance through synergistic coordination and flexible trade-offs among modules.

Technology Category

• Multiagent Systems: Multiagent Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The evolution of Retrieval-Augmented Generation (RAG) has shifted from static retrieval pipelines to dynamic, agentic workflows where a central planner orchestrates multi-turn reasoning. However, existing paradigms face a critical dichotomy: they either optimize modules jointly within rigid, fixed-graph architectures, or empower dynamic planning while treating executors as frozen, black-box tools. We identify that this \textit{decoupled optimization} creates a ``strategic-operational mismatch,''where sophisticated planning strategies fail to materialize due to unadapted local executors, often leading to negative performance gains despite increased system complexity. In this paper, we propose \textbf{JADE} (\textbf{J}oint \textbf{A}gentic \textbf{D}ynamic \textbf{E}xecution), a unified framework for the joint optimization of planning and execution within dynamic, multi-turn workflows. By modeling the system as a cooperative multi-agent team unified under a single shared backbone, JADE enables end-to-end learning driven by outcome-based rewards. This approach facilitates \textit{co-adaptation}: the planner learns to operate within the capability boundaries of the executors, while the executors evolve to align with high-level strategic intent. Empirical results demonstrate that JADE transforms disjoint modules into a synergistic system, yielding remarkable performance improvements via joint optimization and enabling a flexible balance between efficiency and effectiveness through dynamic workflow orchestration.

Problem

• strategic-operational gap
• dynamic agentic RAG
• decoupled optimization
• planning-execution mismatch
• multi-turn reasoning

Innovation

• Joint Optimization
• Dynamic Agentic RAG
• Co-adaptation
• Strategic-Operational Alignment
• Multi-agent Collaboration