StackPlanner: A Centralized Hierarchical Multi-Agent System with Task-Experience Memory Management

StackPlanner introduces a hierarchical multi-agent system that improves coordination among large language model-based agents through explicit memory management and reusable experience learning. The framework addresses critical limitations in centralized multi-agent architectures by decoupling high-level coordination from task execution and enabling agents to retain and leverage past coordination strategies, demonstrating improved performance on complex benchmarks.

StackPlanner represents a significant advancement in multi-agent AI systems architecture, tackling a fundamental problem that has limited practical deployment of LLM-based collaborative agents. Previous centralized systems struggled with context bloat and error accumulation over extended task sequences, forcing developers to choose between limiting agent autonomy or accepting degraded performance. This research directly addresses those constraints by introducing structured memory management and experience retrieval mechanisms that function similarly to how human teams improve through institutional knowledge.

The hierarchical approach separates strategic coordination decisions from tactical task execution, a design pattern borrowed from organizational management. By implementing reinforcement learning to identify reusable coordination patterns, StackPlanner enables agents to generalize insights across different problem domains rather than treating each task in isolation. This is architecturally distinct from simple prompt engineering or few-shot learning approaches, representing a deeper integration of memory and learning into agent behavior.

The implications extend across enterprise automation, scientific research, and complex problem-solving domains where sustained agent collaboration matters. Organizations deploying multi-agent systems for supply chain optimization, research assistance, or software development could benefit from more reliable long-horizon planning. The framework's ability to maintain context quality and reuse successful strategies reduces operational costs and improves output consistency.

Future development likely focuses on scaling StackPlanner to larger agent networks, integrating additional learning modalities beyond reinforcement learning, and testing performance on real-world enterprise workflows. The structured memory approach could also influence how autonomous systems handle knowledge retention across extended operations.

• →StackPlanner's memory management system reduces context bloat and error accumulation in long-horizon multi-agent collaboration.
• →Hierarchical decoupling of coordination from task execution improves reliability and task generalization across different domains.
• →Structured experience memory combined with reinforcement learning enables agents to retrieve and reuse successful coordination strategies.
• →Framework demonstrates measurable improvements on deep-search and agent system benchmarks compared to existing centralized approaches.
• →Architecture addresses a critical bottleneck limiting practical deployment of LLM-based collaborative agent systems in enterprises.