arXiv:2606.01199v1 Announce Type: new Abstract: Large language agents are increasingly used for social simulation, yet it remains unclear whether they can sustain coherent behavior in structured organizations, where goals must propagate through hierarchy, tasks depend on prior execution, and artifacts accumulate over long horizons. We formulate long-horizon organizational simulation as a memory-centered coordination problem and introduce TaskWeave, a hierarchical agentic framework that maintains planning states through a Formulate-Partition-Diagnose-Align cycle and grounds execution through de
The proliferation of large language models and increasing enterprise interest in autonomous agents make the current scalability and organizational integration a critical research area.
This research addresses a core challenge for AI agents moving from isolated tasks to integrated, long-term organizational roles, which is key for advanced automation.
The ability of LLM agents to sustain coherent behavior through hierarchical structures over long durations could significantly accelerate the deployment of autonomous systems in complex enterprises.
- · AI agent developers
- · Enterprises adopting AI automation
- · Software as a Service (SaaS)
- · Tasks requiring manual coordination
- · Legacy workflow management systems
Increased research and development into hierarchical AI agent architectures and memory-centered coordination.
Accelerated adoption of AI agents for complex organizational tasks, leading to efficiency gains and workforce restructuring.
The development of entirely new enterprise structures and business models predicated on highly autonomous, long-horizon AI agent operations.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI