Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 9·5 min read
Skynet: Workflow-Level Anomaly Detection for Agentic AI via Semantic and Structural Modeling
TL;DR
Skynet is a workflow-level anomaly detection framework for agentic AI that identifies task failures by analyzing the entire execution structure. It achieves high recall and low false positive rates, enabling effective online monitoring of AI systems.
✦ Why It Matters
Implement Skynet to enhance anomaly detection in your agentic AI systems today.
Key Takeaways
- Adopt Skynet for comprehensive anomaly detection in AI workflows.
- Utilize directed workflow graphs to analyze execution structures.
- Train models exclusively on benign workflows for better detection.
- Monitor agentic AI systems in real-time with low latency.
How It Works
Skynet transforms the execution of multi-agent tasks into directed workflow graphs, capturing both the semantic context and structural dependencies. By scoring these graphs against learned benign behaviors, it identifies anomalies that deviate from expected patterns.
This approach allows Skynet to detect issues that arise from the interplay of multiple agents and tools, rather than from isolated steps.