Reason-Plan-ReAct: A Reasoner-Planner Supervising a ReAct Executor for Complex Enterprise Tasks

2025-12-03

AI Summary

In enterprise-scale complex tasks, autonomous agents face two key challenges: difficulty in coordinating multiple tools and weak handling of heterogeneous, multi-source data. Monolithic agent architectures yield unstable execution trajectories, while locally deployed open-weight models suffer from limited context windows, causing them to be overwhelmed by large tool outputs. To address these issues, we propose RP-ReAct—a novel multi-agent framework that decouples strategic planning (performed by a large reasoning model–driven Reasoner-Planner Agent) from tool execution (handled by a ReAct-style Proxy-Execution Agent). We further introduce a context preservation mechanism and external storage for managing voluminous tool outputs. Evaluated on the multi-domain ToolQA benchmark using six open-source reasoning models, RP-ReAct consistently outperforms existing methods, demonstrating superior generalization, robustness, execution stability, and adaptability across varying model scales.

Technology Category

• Multiagent Systems: Multiagent Planning
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Despite recent advances, autonomous agents often struggle to solve complex tasks in enterprise domains that require coordinating multiple tools and processing diverse data sources. This struggle is driven by two main limitations. First, single-agent architectures enforce a monolithic plan-execute loop, which directly causes trajectory instability. Second, the requirement to use local open-weight models for data privacy introduces smaller context windows leading to the rapid consumption of context from large tool outputs. To solve this problem we introduce RP-ReAct (Reasoner Planner-ReAct), a novel multi-agent approach that fundamentally decouples strategic planning from low-level execution to achieve superior reliability and efficiency. RP-ReAct consists of a Reasoner Planner Agent (RPA), responsible for planning each sub-step, continuously analysing the execution results using the strong reasoning capabilities of a Large Reasoning Model, and one or multiple Proxy-Execution Agent (PEA) that translates sub-steps into concrete tool interactions using a ReAct approach. Crucially, we incorporate a context-saving strategy within the PEA to mitigate context window overflow by managing large tool outputs via external storage and on-demand access. We evaluate RP-ReAct, on the challenging, multi-domain ToolQA benchmark using a diverse set of six open-weight reasoning models. Our empirical results show that RP-ReAct achieves superior performance and improved generalization ability over state-of-the-art baselines when addressing diverse complex tasks across the evaluated domains. Furthermore we establish the enhanced robustness and stability of our approach across different model scales, paving the way for effective and deployable agentic solutions for enterprises.

Problem

• Autonomous agents struggle with complex enterprise tasks requiring multi-tool coordination.
• Single-agent architectures cause trajectory instability in plan-execute loops.
• Local models' small context windows lead to overflow from large tool outputs.

Innovation

• Multi-agent architecture decouples planning from execution
• Context-saving strategy manages large tool outputs externally
• Uses open-weight reasoning models for data privacy compliance