Can LLM Agents Sustain Long-Horizon Organizational Dynamics?

Researchers introduce TaskWeave, a hierarchical framework that enables large language model agents to maintain coherent behavior in complex organizational simulations over extended periods. The system uses memory-centered coordination and dependency-aware tracking to sustain long-horizon tasks, demonstrating viability for enterprise-level multi-agent applications through year-long IT company simulations.

TaskWeave addresses a fundamental challenge in deploying LLM agents at organizational scale: maintaining coherence when tasks span multiple hierarchical levels, depend on prior execution sequences, and accumulate artifacts over time. Traditional multi-agent frameworks struggle with this complexity because they lack mechanisms to propagate goals through hierarchies and track execution dependencies across long horizons. The paper demonstrates that structured simulation memory—specifically through a Formulate-Partition-Diagnose-Align cycle—serves as a critical infrastructure for reliable agent coordination.

This research builds on growing recognition that LLM agents require architectural innovations beyond prompt engineering to function in structured environments. Prior work in social simulation established that agents can model individual behaviors, but scaling to organizational dynamics introduces coordination problems where individual competence becomes insufficient. TaskWeave's dependency-aware trace memory represents a meaningful innovation in this space, addressing how agents can understand task prerequisites and maintain planning state across time.

For enterprise software development and AI deployment, this work has practical implications. Organizations exploring autonomous agent applications for knowledge work, process automation, and team coordination now have evidence that structured frameworks can sustain multi-agent operations over meaningful timescales. The year-long simulation provides more credible validation than typical short-horizon experiments, though real-world organizational environments introduce additional complexity around human interaction and unexpected external events.

The research signals that production-grade multi-agent systems require intentional design around memory, coordination, and task dependency management rather than relying on emergent behaviors. Future developments likely involve testing TaskWeave against real organizational workflows and measuring how well simulated artifact generation transfers to actual enterprise use cases.

• →TaskWeave maintains coherent organizational behavior in LLM agents through structured simulation memory and dependency-aware task tracking.
• →Year-long IT company simulations demonstrate viability for long-horizon multi-agent coordination beyond typical short-horizon experiments.
• →Hierarchical goal propagation and trace memory are critical mechanisms for reliable LLM-based organizational simulators.
• →The framework outperforms competing multi-agent systems on organizational coherence and execution grounding metrics.
• →Structured memory architecture appears necessary for enterprise-scale LLM agent applications rather than relying on emergent behaviors alone.