The observability blog​

Learn how model observability can help you stay on top of ML in the wild and bring value to your business.

Even industry leaders struggle with AI agent control. Microsoft lost trust with flawed audit logs, Cloudflare and Zscaler faced $2M breaches, and Google’s Gemini was hijacked by calendar invites. This blog unpacks what went wrong, the three failure modes every business must avoid, and how professional governance gives you a competitive edge....

What Microsoft, Google & Cloudflare Learned About AI Agent Control (And How You Can Apply It)

In the coming years, AI governance will be the new frontier of competition. This five-step playbook from SUPERWISE provides a clear roadmap for organizations aiming to achieve professional control over their AI agents. Identity Management: Conduct an agent discovery audit and assign unique identities to each AI agent. This is crucial for preventing "AI sprawl." Real-Time Monitoring: Proactively monitor and set up alerts for critical events and policy breaches to ensure AI security. Policy-Based Guardrails: Implement automated guardrails to enforce policies, which helps scale governance effectively. Secure Integrations: Address the security risks of AI agents accessing sensitive data by assessing API security and defining policies based on the principle of least privilege. Continuous Compliance: Establish that AI compliance is an ongoing process by mapping regulatory requirements and using a continuous audit loop. Following this playbook can lead to significant benefits, including a 75% reduction in security incidents and a 90% improvement in regulatory compliance scores....

The 2026-Ready AI Agent Governance Playbook: 5 Steps to Professional Control

SUPERWISE® V1.24.0 brings production-ready AI governance features to Early Access. From enhanced Agent Studio and full versioning to expert consultation access starting at $0, organizations can now safely scale AI with real-time observability, policy enforcement, and compliance guardrails....

SUPERWISE® V1.24.0 Features NOW AVAILABLE – Enhanced Capabilities for Early Access Users

SUPERWISE has released V1.24.0 and announced the upcoming Starter Edition Early Access for September 15th. The blog post argues that for any organization, from startups to enterprises, AI governance is the key to sustainable growth and avoiding catastrophic failure. The platform-first approach of SUPERWISE provides governance as infrastructure, with features like runtime policy enforcement and compliance by configuration. The document itself is designed to support a strong SEO strategy by: Building Authority: The content is grounded in "operational governance reality," citing research from authoritative sources like IBM, MIT, McKinsey, and Accenture to build expertise and trustworthiness. Targeting Key Scenarios: It uses industry-specific examples from healthcare and manufacturing to address specific pain points and search queries related to "AI governance" and "compliance gaps." Clear Call to Action: The post ends with a "principled invitation" to join the community, creating a clear path for engaged readers and signaling to search engines that the content is valuable and leads to a deeper user experience....

V1.24.0 Released + Governance for Everyone: SUPERWISE® Starter Edition Early Access Coming September 15th

SUPERWISE AI Governance Starter Edition
Projects often fail when AI agents degrade in real-world scenarios. The core problem isn't the model itself, but its runtime operation. This post explores how a platform-first approach operationalizes governance by embedding observability and policies directly at the runtime, ensuring that your AI deployments are reliable, auditable, and adaptable under scrutiny....

Optimizing AI Agent Governance for Scalable Deployments: A Platform-First Approach

SUPERWISE AI Agent Governance for Scalable Deployments
Predictive analytics promised transformation. Businesses invested in systems to forecast demand, detect fraud, and optimize logistics. But in many organizations, those deployments are sitting idle — technically “live,” but forgotten. This isn’t a failure of modeling; it’s a failure of operationalization. For technical teams, the challenge isn’t creating models, it’s keeping them healthy, relevant, and accountable over time with a platform-first approach to runtime observability....

Why Your Predictive Analytics Models Are Gathering Dust

Predictive Analytics Models