Build self-service AWS Health analytics to find actionable health insights with AI agents powered by Amazon Bedrock

The Chaplin solution utilizes AI agents powered by Amazon Bedrock and exposed through the Model Context Protocol (MCP) to provide self-service health event analytics for AWS Health notifications. This approach enables teams to ask questions in natural language and receive precise, contextualized answers without relying on AWS Support. With Chaplin, teams can identify actionable health insights, prioritize events, and make informed decisions. The practical implication for engineers building AI systems is that they can leverage Chaplin to streamline health event management and focus on innovation rather than reactive firefighting.

⚡ Key Takeaways

• Chaplin is an open-source solution that uses AI agents exposed through the Model Context Protocol (MCP) to provide self-service health event analytics.
• The solution utilizes Amazon Bedrock to power the AI agents.
• Teams can interact with Chaplin directly from their AI assistant, such as Claude Code or Kiro CLI, and ask questions in natural language.
• Chaplin can surface and prioritize actionable events, including those linked to AWS Transform templates.
• The solution enables DevOps, security, and operations teams to independently analyze and prioritize health events.

The Chaplin solution has a concrete impact on engineers shipping production AI systems, as it enables them to streamline health event management and focus on innovation. By providing self-service analytics, Chaplin reduces the reliance on AWS Support and Technical Account Managers, allowing teams to make informed decisions quickly.

✅ Practical Steps

1. Deploy Chaplin using the instructions available in the Chaplin AWS Health Agentic Assistant GitHub repository.
2. Integrate Chaplin with your AI assistant, such as Claude Code or Kiro CLI, to enable natural language queries.
3. Use Chaplin to analyze and prioritize health events, and to identify actionable insights for your environment.

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