Amazon Quick now integrates with New Relic for observability-driven AI agents
Amazon Web Services has announced a new integration between Amazon Quick, its AI assistant for workplace tasks, and New Relic's observability platform that enables engineering teams to conduct incident response and root cause analysis directly through conversational AI. The integration allows on-call engineers, site reliability engineers, and engineering leaders to investigate incidents, generate detailed root cause analysis reports, and create tracked tasks without switching between different tools or platforms. The integration leverages New Relic's remote model context protocol (MCP) server to provide Quick users with access to multiple AI-powered capabilities including alert insights, user impact analysis, log analysis, transaction diagnostics, and natural language NRQL queries. Engineers can now investigate incidents across their observability data, automatically generate root cause analysis documents with supporting evidence links, and distribute findings via email attachments—all within a single chat interface. Additionally, Quick Flows can invoke New Relic's AI agents to automate recurring incident triage runbooks and escalation workflows. The integration combines real-time telemetry data from New Relic with organizational knowledge stored in Amazon Quick Spaces, such as runbooks, architecture documentation, and on-call policies, to provide contextually relevant responses. This capability is now available across all AWS regions where Amazon Quick is supported, offering engineering teams a streamlined approach to incident management and observability-driven operations.
Why It Matters
This integration represents a significant advancement in observability-driven incident response by eliminating context switching between tools and enabling natural language interactions with complex monitoring data. For enterprise DevOps teams, this could dramatically reduce mean time to resolution (MTTR) by allowing engineers to query observability data, correlate incidents, and generate documentation through conversational AI rather than navigating multiple dashboards and interfaces. The combination of real-time telemetry with organizational knowledge also addresses a key challenge in incident response—ensuring that automated analysis reflects both technical data and company-specific procedures and policies.
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