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In this post, we show you how to build Chaplin (Customer Health and Planned Lifecycle Intelligence Nexus), an open source solution that uses AI agents exposed through the Model Context Protocol (MCP) to provide self-service health event analytics.
On a typical Monday morning, an enterprise operations team receives multiple AWS Health notifications about Amazon Linux 2 end-of-life, RDS version deprecations, and EC2 instance retirements across 50+ accounts. Without self-service analytics, the team has no way to quickly identify the events that affect production systems, the events that require immediate action versus long-term planning, and the business impact of each event category.
Operations teams also spend time waiting for Technical Account Managers (TAMs) to interpret health events, adding delays to critical operational decisions. The result is time spent on reactive firefighting rather than innovation.
In this post, we show you how to build Chaplin (Customer Health and Planned Lifecycle Intelligence Nexus), an open source solution that uses AI agents exposed through the Model Context Protocol (MCP) to provide self-service health event analytics. With Chaplin, teams can ask questions in natural language directly from MCP-compatible AI assistants and receive precise, contextualized answers without depending on AWS Support for routine analysis. Detailed deployment instructions are available in the Chaplin AWS Health Agentic Assistant GitHub repository.
Enterprises running production workloads on AWS manage a constant stream of health events – service changes, maintenance windows, security patches, and operational notifications – across dozens or hundreds of accounts. AWS Health provides comprehensive event data through the AWS Health API and Amazon EventBridge, but reactive management approaches leave gaps.
Eligible Health events will soon be linked directly to AWS Transform templates, enabling customers to act on events directly. Chaplin can surface and prioritize these actionable events for your environment.
Chaplin implements self-service health event analytics using agentic AI powered by Amazon Bedrock, delivered through the Model Context Protocol (MCP). Instead of predefined dashboard schemas, Chaplin exposes AI-powered tools that MCP-compatible clients can consume. Teams interact with Chaplin directly from their AI assistant – such as Claude Code or Kiro CLI – and ask questions in natural language. For example, a team member might ask for upcoming RDS lifecycle events in the next 60 days, request a summary of open EC2 events prioritized by urgency, query security patches affecting production environments, or check which maintenance windows could affect high-priority applications.