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. 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. Together, these meant engineers spent their time on manual coordination instead of resolving issues. The solution: Orion AI 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. Amazon Bedrock provided managed access to foundation models, and Strands Agents provided the orchestration layer. Results Orion AI delivered measurable improvements in both diagnosis speed and accuracy and freed the team from manual coordination. By automating manual bottlenecks and streamlining cross-team workflows, Cornerstone achieved the following outcomes. Metric Before After Improvement Database diagnosis time 45 minutes 10 minutes 78% faster Manual lifecycle steps 10+ steps 1 interaction 70% reduction SRE-to-data-team reporting lag 15 minutes Instantaneous Real-time Redundant alerts High baseline volume Filtered 65% reduction (median) Faster performance troubleshooting Reducing database diagnosis time was Orion AI’s largest single efficiency gain to date. Previously, engineers manually connected to the affected SQL Server instance, queried system views for blocking chains and wait types, cross-referenced logs to isolate long-running queries, and correlated evidence to form a root cause hypothesis. This averaged approximately 45 minutes per incident. With Orion AI, three specialized agents share the investigation, each owning a distinct stage. Beyond diagnosis, Orion AI carries issues through remediation: identifying the root cause, recommending the fix, and creating a populated Jira ticket assigned to the right on-call engineer. A four-step manual handoff collapses into a single interaction. Automated database lifecycle management Tasks that previously required over 10 manual steps, from establishing database connections to running cross-system queries to communicating status updates, now run as a single natural language interaction. Orion AI identifies the right tools and data sources, runs queries across systems, validates results, and returns a unified response. From the engineer’s perspective, the work is reduced to prompting Orion AI and validating the findings it reports back. Real-time monitoring and reduced alert fatigue Orion AI removed the 15-minute SRE-to-data-team reporting lag, replacing periodic manual checks with continuous cross-system visibility. Orion AI reduced redundant alerts by a median of 65 percent through deduplication, threshold filtering, and cross-signal correlation. For every 10 alerts previously generated, only 3–4 now reach engineers. Alerts route directly to the responsible teams without an intermediate alerting layer. Key design principles Three decisions shaped Orion AI, and you can apply them to your own multi-agent projects: Split agents by domain, not by task complexity : Each agent carries a narrow set of tool integrations for one operational domain. This keeps a model’s context focused and improves the accuracy of tool selection, rather than asking one general-purpose agent to do everything. Default to keyword routing, fall back to semantic search : Predictable requests are matched by keyword for speed. Ambiguous requests fall back to semantic search for accuracy. This keeps latency low without sacrificing correctness on the harder queries. Scope conversational memory to sessions and bypass it for live metrics : Persistent memory supports multi-turn conversations, but operational questions always read current system state. Bypassing memory for live metrics helps prevent stale data from contaminating real-time diagnostics. Architecture overview Orion AI is deployed as containerized services on Amazon Elastic Container Service (Amazon ECS). Users interact through a web application that routes each request to the appropriate agent, invokes the required tools, and assembles the response. The following diagram shows how these components connect within the AWS Cloud. Figure 1: Orion AI architecture: a meta-orchestrator on Amazon ECS routes requests to specialized agents, which reach data sources through a Portal-Tools MCP server and use Amazon Bedrock for models, long-term memory, and retrieval augmented generation Architecture deep dive The preceding design principles show up concretely in the architecture. The rest of this section walks through Orion AI’s core components. Building the hub-and-spoke pattern with Strands Agents Orion AI expresses its topology with a handful of Strands Agents primitives. The Agent class is the building block for both specialist agents and the meta-orchestrator. Each agent’s tools are ordinary Python functions marked with the @tool decorator. This derives a tool’s specification from the function’s type hints and docstring, meaning there is no separate schema to maintain. BedrockModel wraps the Amazon Bedrock model each agent runs on, and MCPClient connects agents to external tool servers. The topology: The hub is a single Strands Agent acting as the meta-orchestrator (the TaskExecutor). It holds no domain tools — only routing tools ( find_relevant_agents , call_agent ) and control-flow tools ( emit_plan_step , emit_confirmation_gate ) so it can reason through a request step by step. The spokes are independent Agent instances, loaded lazily through a decorator-based registry. Each carries its own model, domain tools, and localized system prompt. At runtime, the hub invokes a specialist by name through the registered call_agent tool. Each specialist runs its own tool-calling loop and returns synthesized text back to the hub, which assembles the final response. Hybrid routing Orion AI uses keyword-first routing with a semantic search fallback. Approximately 80 percent of queries resolve through an in-memory keyword fast-path in under a millisecond. When keywords are insufficient to determine intent, the system falls back to semantic search powered by Amazon Titan Text Embeddings V2 to route by meaning. For model availability by AWS Region,