Telmai Released Data Reliability Workload for Microsoft Fabric

The new tool uses AI agents to monitor data quality and volume within Microsoft's OneLake ecosystem.

Updated on Sept. 29, 2026 in Artificial Intelligence

Isometric editorial illustration of interconnected geometric data blocks in teal and mustard, representing digital information reliability and systemic order. — Telmai launched a data reliability tool for Microsoft Fabric, using AI agents to monitor schema, freshness, and completeness of critical business assets. AI Illustration. Upload story photo >

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Do you trust automated AI agents to make decisions based on corporate data?

Telmai has launched a data reliability workload specifically designed for Microsoft Fabric. The platform uses AI-powered agents to monitor schema, freshness, and completeness for critical business assets.

Why it matters

As organizations shift to complex data environments, traditional tools often fail to maintain quality standards. This integration ensures that AI agents can access reliable data, preventing errors caused by faulty information.

The system utilizes AI agent-monitors to track datasets within the OneLake Catalog for Delta Lake and Apache Iceberg tables. It further exposes real-time trust signals via an MCP server to support AI-driven workflows.

The players

Telmai

A technology company specializing in automated data observability and quality monitoring solutions.

Microsoft Fabric

An end-to-end analytics platform that integrates data engineering, data science, and real-time analytics for organizations.

The details

The workload integrates directly with the OneLake Catalog to automatically identify and prioritize business-critical assets for continuous monitoring. By generating real-time trust signals, the platform allows for seamless integration with communication tools like Microsoft Teams and project management platforms like Jira.

September 29, 2026: Telmai officially released its workload for Microsoft Fabric.

The Tech Race

This release marks a shift toward AI-native observability as businesses move away from traditional, rules-based quality tools that struggle in federated ecosystems. It positions Telmai as a key provider of the trust infrastructure required to support autonomous AI agents.

Data engineers and analysts can now automate quality checks that previously required manual oversight or rigid coding. By integrating trust signals into Jira and Microsoft Teams, teams can receive real-time alerts about data issues without switching platforms.

The takeaway

Reliable data is the primary requirement for effective AI performance in corporate settings. Organizations should prioritize observability tools that integrate directly into their existing project management workflows to maintain operational trust.