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How Cornerstone OnDemand cut database diagnosis by 78% with Amazon Bedrock

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How Cornerstone OnDemand cut database diagnosis by 78% with Amazon Bedrock

Cornerstone OnDemand, Inc. (Cornerstone) is a global leader in workforce readiness solutions, serving 140 million users across 186 countries. The company built a multi-agent AI system that transforms database operations from reactive firefighting into proactive, self-orchestrating workflows. The system, called Orion AI, uses Amazon Bedrock and Strands Agents, an open source agent orchestration framework from AWS, to coordinate specialized agents.

Before Orion AI, Cornerstone’s Enterprise DataOps team would spend up to 45 minutes per database incident manually querying system views and cross-referencing logs before handing off across teams. With Orion AI, database diagnosis dropped from 45 minutes to 10, a 78% reduction. A three-person team delivered the system in six months.

In this post, we walk through the operational problem Cornerstone faced, how they designed Orion AI, the outcomes they measured, and the design decisions other teams can reuse.

Before Orion AI, Cornerstone’s Enterprise DataOps team operated reactively, with four recurring pain points:

Together, these meant engineers spent their time on manual coordination instead of resolving issues.

Orion AI is a multi-agent system where a coordinating agent (the meta-orchestrator) delegates work to specialized child agents arranged in a hub-and-spoke topology. Engineers interact with it through a web application, asking questions and approving actions in the same place they already coordinate operational work. The agents cover infrastructure monitoring, database diagnostics, database lifecycle operations, customer analytics, and knowledge-driven support.

Two tenets guided the build: data privacy using AWS controls under the shared responsibility model , and deep integration with existing operational tools.


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