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.

The operational challenge

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

• •Database performance investigations took approximately 45 minutes per incident, spread across multiple tools and system views.
• •Database lifecycle workflows required 10 or more manual steps, from establishing connections to communicating status updates.
• •Reporting between the site reliability engineering (SRE) team and the data team ran on a 15-minute lag.
• •Redundant and overlapping alerts created noise that buried the signals that mattered.