Observability & Evaluation · Software component
Agent Interaction Graph Analyzer
Software componentObservability & EvaluationObservability & Evaluationarc:AgentInteractionGraphAnalyzer
A monitoring component that builds the inter-agent communication graph and computes centrality, clustering and community metrics to detect unusual collaboration structures, bottlenecks and coordinated anomalies invisible in per-agent metrics.
Responsibility. Detects emergent multi-agent behaviour anomalies from interaction patterns.
Also known as: Interaction graph analysis, Collective behavior monitoring, Cross-agent correlation
Relationships
Design guidance
- SHOULD supplement per-agent monitoring for multi-agent systems, since each agent may look normal while collective behaviour is harmful.
Quantitative guidance
As stated by the sources; verify before use.
- Example signal: an agent suddenly communicating with ten times its normal peer count (Ch10.4).
Classification
- Patterns
- Graph centrality analysisCommunity detection
- Quality attributes
- Maintainability (ISO/IEC 25010)Safety (ISO/IEC 25010 | NIST AI RMF: safe)
- Risks mitigated
- Emergent system-level bottlenecksCascading failuresCollusionCompromised agents seeking inappropriate information
Sources
- Ch10.4: T. Nguyen, "Human-in-the-Loop," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.4. ISBN: 9798244538229.