A Task-Property–Driven Dataset for Predicting Coordination Failures and Scaling Limits

TL;DR: Like building a giant “black box” flight recorder for agent teams, this project would collect massive logs of agent interactions and task features to train models that predict when and why coordination breaks down. An initial dataset would pair diverse multi-agent tasks with high-resolution coordination logs and failure annotations.

Research Question: Can we construct a large-scale, open dataset capturing agent interactions, coordination metrics, task properties, and failure events to enable predictive modeling of coordination failures and scaling bottlenecks?

Hypothesis: Such a dataset will reveal new, generalizable patterns (e.g., early warning signals, topology-task interactions) not captured by small-scale studies, enabling more robust predictive models for optimal architecture selection and proactive failure mitigation.

Experiment Plan: - Data Collection: Instrument a wide range of multi-agent tasks (from the original paper and new domains: robotics, finance, web navigation), logging fine-grained agent actions, communications, and task/environment properties.

• Annotation: Mark episodes with coordination failures, error amplification, or performance drops and label task features (parallelizability, tool reliance, etc.).
• Modeling: Train supervised and unsupervised models to classify and predict coordination breakdowns based on early signals.
• Release: Provide the dataset to the community as a benchmark for the science of agentic scaling.

References:

• Muhammad Althaf, A., Ahmed Mohammed, M., Milanova, M., Talburt, J., & Cakmak, M. C. (2025). Multi-Agent RAG Framework for Entity Resolution: Advancing Beyond Single-LLM Approaches with Specialized Agent Coordination. Computers.
• Dvorkin, V. (2023). Agent Coordination via Contextual Regression (AgentCONCUR) for Data Center Flexibility. IEEE Transactions on Power Systems.
• Nalagatla, G. (2025). Hierarchical Decentralized Multi-Agent Coordination with Privacy-Preserving Knowledge Sharing: Extending AgentNet for Scalable Autonomous Systems.