Enterprise AI evaluations systematically monitor, measure, and assess AI agent performance, quality, and compliance by collecting trace data across an organization's AI landscape.Enterprises are increasingly deploying AI solutions across various platforms, including AWS, Google Cloud Platform (GCP), and Microsoft Azure. AI agents generate operational traces across multiple hyperscalers and observability platforms, leading to fragmented monitoring data and lack of centralized evaluation capabilities.
Through this capability, the administrator can configure trace collection integrations with hyperscaler and cloud-native observability environments to offer a unified pipeline for enterprise AI trace ingestion. This enables AI steward to evaluate AI systems like AI Agents operating within the enterprise ecosystem by monitoring performance metrics, quality indicators, and other KPIs which are generated by ServiceNow observability service.
- Multi-Cloud Trace Collection - Seamlessly collect AI agent trace data from AWS Agent Core and Google Cloud Platform (GCP) through a single unified interface without the complexity of SDKs
- Guided Trace Configuration - Set up trace collection in minutes using a guided workflow that walks you through various steps for each cloud hyperscaler. ServiceNow MID Servers provide secure, authenticated access to your cloud platforms while keeping sensitive credentials protected.
- Automated Metric Generation - Your collected traces are transported to observability service which generates actionable evaluation metrics. View real-time insights into AI agent performance, quality, cost, and compliance directly in AI Control Tower, empowering you to make data-driven governance decisions.
- Net-new feature of Multi-Cloud Trace Collection
AI Control Tower Plugin (com.sn_aict)
App Dependencies:
- sn_ai_governance (5.1.4)
- sn_ai_disc (2.0.5)