Multi-Agent Oversight

Extend VIGIL to supervise and coordinate across multiple LLM agents by addressing priority resolution, cross-agent causal inference, and aggregate state modeling to achieve population-level reflective runtime governance.

Background

The system is presented as an out-of-band reflective runtime focused on a single agent’s logs and affective state. Scaling this approach to multi-agent environments introduces nontrivial coordination and inference challenges.

The authors highlight specific obstacles—prioritizing issues across agents, inferring causal relationships between agent behaviors, and modeling aggregate state—that must be solved to realize population-level supervision in distributed agent systems.