Towards a Science of Scaling Agent Systems

Multi-agent performance depends on task parallelism, sequential dependencies, tool density, and coordination topology rather than simple scale.

I analyze how decomposing complex workflows into role-constrained specialist agents coordinated by a supervisor state-machine can improve task execution when coordination overhead is properly managed.

Conceptual application: I designed a multi-agent supervisor topology in LangGraph to separate intent classification from downstream CRM payload construction.

Literature analysis of arXiv:2512.08296 and architectural design studies.

• • Inter-agent communication latency increases with graph depth.
• • Requires strict schema validation at state handoffs to prevent error propagation.