Site reliability engineers (SREs) are increasingly faced with challenges in managing modern distributed systems. During incidents, they must quickly analyze data from logs, metrics, Kubernetes events, and documentation to find root causes and implement fixes. Traditional monitoring tools often provide only raw data, necessitating manual investigation by SREs.

Generative AI can change this. By leveraging natural language queries, SREs can ask questions like “Why are the payment-service pods crash looping?” and get actionable insights that combine various data sources, thus transforming incident response from a laborious process into a collaborative and efficient one.

This blog post illustrates how to create a multi-agent SRE assistant using Amazon Bedrock AgentCore, LangGraph, and the Model Context Protocol (MCP). The solution features specialized AI agents that collaborate to deliver the in-depth insights SRE teams need for effective incident management. We’ll outline steps from demo environment setup to deploying on Amazon Bedrock AgentCore Runtime.

The architecture incorporates four AI agents, each specializing in various SRE tasks, that work under a supervisor agent to assist with infrastructure analysis and incident response. Using synthetically generated data from a demo environment, this system highlights capabilities like natural language queries, multi-agent collaboration, real-time data synthesis, automated runbook execution, and source attribution for verification.

The deployment process seamlessly utilizes Amazon Bedrock AgentCore primitives, ensuring secure communication, session continuity, and observability. As we progress through implementation, we will also address memory strategies that enhance personalized user experiences and detail how the system operationalizes observability for enhanced monitoring through Amazon CloudWatch.

This comprehensive solution aims to empower SREs, enabling faster incident resolution and reducing downtime by providing efficient, collaborative, and intelligent incident response processes. For further details and a complete implementation guide, visit our GitHub repository.